Binesh Sadanandan PhD Dissertation Companion
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Every result, traceable to its source

43 results and 20 charts from the dissertation. Filter by thrust, model, dataset, metric, or population. Each one opens onto the sample it covers and the file it was computed from.

One thing to know before comparing numbers The thrusts run on different evaluation sets, so two flip rates are not always about the same thing: MedGemma-4B flips 8.3% of pairs on the benchmark and 42.1% on the curated diagnostic set, and both are correct. Every panel names its population, and the population reference lists them.

Showing all 20 charts and 43 results

Pairwise paraphrase flip rate by model and dataset

Six base vision-language models on the equivalence-filtered PSF-Med binary yes/no subset: MIMIC-CXR 1,539 questions / 5,076 pairs, PadChest 8,445 / 36,244, VinDr-CXR 2,807 / 8,612.

0.014.028.042.056.070.0MIMIC-CXRPadChestVinDr-CXRpercent (%)MedGemma-1.5-4B — MIMIC-CXR: 7.4% (n=5,076)MedGemma-4B — MIMIC-CXR: 8.3% (n=5,076)LLaVA-Rad — MIMIC-CXR: 15.6% (n=5,076)MedGemma-27B — MIMIC-CXR: 6.4% (n=5,076)CheXone — MIMIC-CXR: 8.2% (n=5,076)RadFM — MIMIC-CXR: 13.7% (n=5,076)MedGemma-1.5-4B — PadChest: 17.8% (n=36,244) · dissertation Table 3.3 value; the v2 recompute file gives 17.47%MedGemma-4B — PadChest: 13.4% (n=36,244)LLaVA-Rad — PadChest: 0.8% (n=36,244) · degenerate yes-bias cell (over 98% positive on originals)!MedGemma-27B — PadChest: 13.9% (n=36,244) · no cell in the v2 recompute file; dissertation table onlyCheXone — PadChest: 12.0% (n=36,244) · dissertation Table 3.3 value; the v2 recompute file gives 13.04%RadFM — PadChest: 32.8% (n=34,733) · run covers 8,092 of the table's questions (34,733 pairs)MedGemma-1.5-4B — VinDr-CXR: 0.8% (n=8,612) · degenerate yes-bias cell (over 98% positive on originals)!MedGemma-4B — VinDr-CXR: 15.3% (n=8,612)LLaVA-Rad — VinDr-CXR: 11.6% (n=8,612)MedGemma-27B — VinDr-CXR: 8.1% (n=8,612)CheXone — VinDr-CXR: 11.1% (n=8,612)RadFM — VinDr-CXR: 54.7% (n=8,612)
grouped-bar chart. 18 values across 6 series. Highest: RadFM at VinDr-CXR, 54.7%. Lowest: MedGemma-1.5-4B at VinDr-CXR, 0.8%. The full values are in the data table.
  • MedGemma-1.5-4B
  • MedGemma-4B
  • LLaVA-Rad
  • MedGemma-27B
  • CheXone
  • RadFM

Query-level flip rate: at least one paraphrase flips

Same PSF-Med binary subset as the pairwise chart, re-scored per question (MIMIC-CXR n=1,539, PadChest n=8,445, VinDr-CXR n=2,807 questions). MedGemma-27B on PadChest is absent from the recompute file and is not plotted.

0.016.032.048.064.080.0MIMIC-CXRPadChestVinDr-CXRpercent (%)MedGemma-1.5-4B — MIMIC-CXR: 11.0% (n=1,539)MedGemma-4B — MIMIC-CXR: 18.1% (n=1,539)LLaVA-Rad — MIMIC-CXR: 30.1% (n=1,539)MedGemma-27B — MIMIC-CXR: 13.6% (n=1,539)CheXone — MIMIC-CXR: 14.6% (n=1,539)RadFM — MIMIC-CXR: 25.1% (n=1,539)MedGemma-1.5-4B — PadChest: 43.6% (n=8,445)MedGemma-4B — PadChest: 32.2% (n=8,445) · dissertation Table 3.4 value; the recompute file gives 32.1%LLaVA-Rad — PadChest: 2.5% (n=8,445) · degenerate yes-bias cell (over 98% positive on originals)!CheXone — PadChest: 30.2% (n=8,445)RadFM — PadChest: 68.4% (n=8,092)MedGemma-1.5-4B — VinDr-CXR: 1.9% (n=2,807) · degenerate yes-bias cell (over 98% positive on originals)!MedGemma-4B — VinDr-CXR: 28.5% (n=2,807)LLaVA-Rad — VinDr-CXR: 17.7% (n=2,807)MedGemma-27B — VinDr-CXR: 17.0% (n=2,807)CheXone — VinDr-CXR: 24.9% (n=2,807)RadFM — VinDr-CXR: 60.7% (n=2,807)
grouped-bar chart. 17 values across 6 series. Highest: RadFM at PadChest, 68.4%. Lowest: MedGemma-1.5-4B at VinDr-CXR, 1.9%. The full values are in the data table.
  • MedGemma-1.5-4B
  • MedGemma-4B
  • LLaVA-Rad
  • MedGemma-27B
  • CheXone
  • RadFM

Accuracy against pairwise flip rate

17 model-dataset cells (six base models x three datasets; MedGemma-27B / PadChest absent) on the PSF-Med binary subset, 5,076 / 36,244 / 8,612 pairs.

0.00.040.014.080.028.0120.042.0160.056.0200.070.0Pairwise flip rate (%)Accuracy (%)MIMIC-CXR (medgemma-15-4b): flip 7.4%, accuracy 57.1% (n=5,076)PadChest (medgemma-15-4b): flip 17.5%, accuracy 73.1% (n=36,244)VinDr-CXR (medgemma-15-4b — degenerate yes-bias cell (over 98% positive on originals)): flip 0.8%, accuracy 100.0% (n=8,612)MIMIC-CXR (medgemma-4b): flip 8.3%, accuracy 82.7% (n=5,076)PadChest (medgemma-4b): flip 13.3%, accuracy 41.4% (n=36,244)VinDr-CXR (medgemma-4b): flip 15.3%, accuracy 50.0% (n=8,612)MIMIC-CXR (llava-rad): flip 15.6%, accuracy 64.9% (n=5,076)PadChest (llava-rad — degenerate yes-bias cell (over 98% positive on originals)): flip 0.7%, accuracy 83.3% (n=36,244)VinDr-CXR (llava-rad): flip 11.6%, accuracy 85.8% (n=8,612)MIMIC-CXR (medgemma-27b): flip 6.4%, accuracy 76.8% (n=5,076)VinDr-CXR (medgemma-27b): flip 8.1%, accuracy 66.5% (n=8,612)MIMIC-CXR (chexone): flip 8.2%, accuracy 68.6% (n=5,076)PadChest (chexone): flip 13.0%, accuracy 41.3% (n=36,244)VinDr-CXR (chexone): flip 11.0%, accuracy 28.8% (n=8,612)MIMIC-CXR (radfm): flip 13.7%, accuracy 49.0% (n=5,076)PadChest (radfm): flip 32.8%, accuracy 40.6% (n=34,733)VinDr-CXR (radfm): flip 54.7%, accuracy 60.2% (n=8,612)
scatter chart. 17 values across 3 series. Highest: VinDr-CXR at 0.78, 100.0%. Lowest: VinDr-CXR at 11.03, 28.8%. The full values are in the data table.
  • MIMIC-CXR
  • PadChest
  • VinDr-CXR

How often the answer survives deleting the image

Two separate populations, never to be compared with each other: the 107-pair three-backend diagnostic set (MedGemma-4B, MedGemma-27B, LLaVA-Rad) and the clean patient-disjoint safety re-audit, presence-of-finding endpoint, n=241 questions (base, Targeted LoRA over 5 seeds, Full LoRA over 3 seeds).

0.040.080.0120.0160.0200.0Three-backend diagnostic set (n=107 pairs)Patient-disjoint safety re-audit (n=241 questions)percent (%)MedGemma-4B — Three-backend diagnostic set (n=107 pairs): 66.4% (n=107)MedGemma-27B — Three-backend diagnostic set (n=107 pairs): 85.0% (n=107)LLaVA-Rad — Three-backend diagnostic set (n=107 pairs): 96.3% (n=107)MedGemma-4B — Patient-disjoint safety re-audit (n=241 questions): 53.5% (n=241) · base model, presence-of-finding endpointTargeted LoRA — Patient-disjoint safety re-audit (n=241 questions): 76.8% (n=241) · mean +/- 1 sd over 5 seedsFull LoRA — Patient-disjoint safety re-audit (n=241 questions): 76.6% (n=241) · mean +/- 1 sd over 3 seeds
bar chart. 6 values across 5 series. Highest: LLaVA-Rad at Three-backend diagnostic set (n=107 pairs), 96.3%. Lowest: MedGemma-4B at Patient-disjoint safety re-audit (n=241 questions), 53.5%. The full values are in the data table.
  • MedGemma-4B
  • MedGemma-27B
  • LLaVA-Rad
  • Targeted LoRA
  • Full LoRA

Four-quadrant safety screen: consistency against image reliance

Ten model-dataset settings on the curated behavioural flip banks: MedGemma variants on MIMIC-CXR n=98 and PadChest n=861; LLaVA-Rad variants on MIMIC-CXR n=88 and PadChest n=732 (its text-only-baseline subset).

0.020.040.060.080.0100.0Base MedGemma-4B / MIMIC-CXRBase MedGemma-4B / PadChestTargeted LoRA / MIMIC-CXRTargeted LoRA / PadChestFull LoRA / MIMIC-CXRFull LoRA / PadChestLLaVA-Rad base / MIMIC-CXRLLaVA-Rad base / PadChestLLaVA-Rad LoRA / MIMIC-CXRLLaVA-Rad LoRA / PadChestpercent (%)Ideal (consistent, image-reliant), Base MedGemma-4B / MIMIC-CXR: 31.6%Fragile (inconsistent, image-reliant), Base MedGemma-4B / MIMIC-CXR: 13.3%Dangerous (consistent, text-reliant), Base MedGemma-4B / MIMIC-CXR: 25.5%Worst (inconsistent, text-reliant), Base MedGemma-4B / MIMIC-CXR: 29.6%Ideal (consistent, image-reliant), Base MedGemma-4B / PadChest: 3.5%Fragile (inconsistent, image-reliant), Base MedGemma-4B / PadChest: 19.5%Dangerous (consistent, text-reliant), Base MedGemma-4B / PadChest: 22.5%Worst (inconsistent, text-reliant), Base MedGemma-4B / PadChest: 54.5%Ideal (consistent, image-reliant), Targeted LoRA / MIMIC-CXR: 21.4%Fragile (inconsistent, image-reliant), Targeted LoRA / MIMIC-CXR: 13.3%Dangerous (consistent, text-reliant), Targeted LoRA / MIMIC-CXR: 59.2%Worst (inconsistent, text-reliant), Targeted LoRA / MIMIC-CXR: 6.1%Ideal (consistent, image-reliant), Targeted LoRA / PadChest: 2.7%Fragile (inconsistent, image-reliant), Targeted LoRA / PadChest: 7.7%Dangerous (consistent, text-reliant), Targeted LoRA / PadChest: 84.1%Worst (inconsistent, text-reliant), Targeted LoRA / PadChest: 5.6%Ideal (consistent, image-reliant), Full LoRA / MIMIC-CXR: 17.3%Fragile (inconsistent, image-reliant), Full LoRA / MIMIC-CXR: 2.0%Dangerous (consistent, text-reliant), Full LoRA / MIMIC-CXR: 78.6%Worst (inconsistent, text-reliant), Full LoRA / MIMIC-CXR: 2.0%Ideal (consistent, image-reliant), Full LoRA / PadChest: 16.3%Fragile (inconsistent, image-reliant), Full LoRA / PadChest: 7.7%Dangerous (consistent, text-reliant), Full LoRA / PadChest: 60.6%Worst (inconsistent, text-reliant), Full LoRA / PadChest: 15.4%Ideal (consistent, image-reliant), LLaVA-Rad base / MIMIC-CXR: 2.3%Fragile (inconsistent, image-reliant), LLaVA-Rad base / MIMIC-CXR: 18.2%Dangerous (consistent, text-reliant), LLaVA-Rad base / MIMIC-CXR: 62.5%Worst (inconsistent, text-reliant), LLaVA-Rad base / MIMIC-CXR: 17.1%Ideal (consistent, image-reliant), LLaVA-Rad base / PadChest: 0.0%Fragile (inconsistent, image-reliant), LLaVA-Rad base / PadChest: 10.4%Dangerous (consistent, text-reliant), LLaVA-Rad base / PadChest: 79.5%Worst (inconsistent, text-reliant), LLaVA-Rad base / PadChest: 10.1%Ideal (consistent, image-reliant), LLaVA-Rad LoRA / MIMIC-CXR: 21.6%Fragile (inconsistent, image-reliant), LLaVA-Rad LoRA / MIMIC-CXR: 10.2%Dangerous (consistent, text-reliant), LLaVA-Rad LoRA / MIMIC-CXR: 58.0%Worst (inconsistent, text-reliant), LLaVA-Rad LoRA / MIMIC-CXR: 10.2%Ideal (consistent, image-reliant), LLaVA-Rad LoRA / PadChest: 16.0%Fragile (inconsistent, image-reliant), LLaVA-Rad LoRA / PadChest: 7.1%Dangerous (consistent, text-reliant), LLaVA-Rad LoRA / PadChest: 56.7%Worst (inconsistent, text-reliant), LLaVA-Rad LoRA / PadChest: 20.2%
stacked-bar chart. 40 values across 4 series. Highest: Dangerous (consistent, text-reliant) at Targeted LoRA / PadChest, 84.1%. Lowest: Ideal (consistent, image-reliant) at LLaVA-Rad base / PadChest, 0.0%. The full values are in the data table.
  • Ideal (consistent, image-reliant)
  • Fragile (inconsistent, image-reliant)
  • Dangerous (consistent, text-reliant)
  • Worst (inconsistent, text-reliant)

Image-swap sensitivity: how often a contradicting image changes the answer

One population only: the 861-question PadChest flip bank, four models (base MedGemma-4B, Targeted LoRA, Full LoRA, LLaVA-Rad). MIMIC-CXR cells are deliberately excluded so that no cross-population comparison is possible.

0.010.020.030.040.050.0PadChest flip bankpercent (%)MedGemma-4B — PadChest flip bank: 39.5% (n=861)Targeted LoRA — PadChest flip bank: 31.5% (n=861)Full LoRA — PadChest flip bank: 28.2% (n=861)LLaVA-Rad — PadChest flip bank: 14.5% (n=861)
bar chart. 4 values across 4 series. Highest: MedGemma-4B at PadChest flip bank, 39.5%. Lowest: LLaVA-Rad at PadChest flip bank, 14.5%. The full values are in the data table.
  • MedGemma-4B
  • Targeted LoRA
  • Full LoRA
  • LLaVA-Rad

LoRA flip reduction, seed by seed

Clean patient- and image-disjoint MIMIC-CXR test, presence-of-finding endpoint: 238 questions / 991 pairs, 236 held-out subjects, zero subject and zero image overlap with training. Targeted LoRA seeds 42/123/456/789/2024; Full LoRA seeds 42/123/456.

0.02.04.06.08.010.0421234567892024percent (%)MedGemma-4B — 42: 8.5% (n=991) · base model; identical in every seed (the adapter is the only thing that varies)Targeted LoRA — 42: 2.7% (n=991) · McNemar exact two-sided p = 2.5e-07Full LoRA — 42: 2.6% (n=991) · McNemar exact two-sided p = 5.7e-05MedGemma-4B — 123: 8.5% (n=991) · base model; identical in every seed (the adapter is the only thing that varies)Targeted LoRA — 123: 3.6% (n=991) · McNemar exact two-sided p = 1.2e-03Full LoRA — 123: 3.1% (n=991) · McNemar exact two-sided p = 1.3e-03MedGemma-4B — 456: 8.5% (n=991) · base model; identical in every seed (the adapter is the only thing that varies)Targeted LoRA — 456: 3.3% (n=991) · McNemar exact two-sided p = 1.8e-04Full LoRA — 456: 3.0% (n=991) · McNemar exact two-sided p = 2.4e-05MedGemma-4B — 789: 8.5% (n=991) · base model; identical in every seed (the adapter is the only thing that varies)Targeted LoRA — 789: 3.4% (n=991) · McNemar exact two-sided p = 5.5e-06MedGemma-4B — 2024: 8.5% (n=991) · base model; identical in every seed (the adapter is the only thing that varies)Targeted LoRA — 2024: 4.4% (n=991) · McNemar exact two-sided p = 1.8e-03
dot chart. 13 values across 3 series. Highest: MedGemma-4B at 42, 8.5%. Lowest: Full LoRA at 42, 2.6%. The full values are in the data table.
  • MedGemma-4B
  • Targeted LoRA
  • Full LoRA

Accuracy on the clean patient-disjoint endpoint, with 95% Wilson intervals

Presence-of-finding endpoint, n=238 questions per arm on the patient- and image-disjoint MIMIC-CXR test (236 held-out subjects, zero subject and zero image overlap with training). One point per adapter seed.

0.040.080.0120.0160.0200.0Base (no adapter)Targeted LoRA seed 42Targeted LoRA seed 123Targeted LoRA seed 456Targeted LoRA seed 789Targeted LoRA seed 2024Full LoRA seed 42Full LoRA seed 123Full LoRA seed 456percent (%)MedGemma-4B — Base (no adapter): 84.5% [79.3, 88.5] (n=238) · 201/238 correctTargeted LoRA — Targeted LoRA seed 42: 85.3% [80.2, 89.2] (n=238) · 203/238 correctTargeted LoRA — Targeted LoRA seed 123: 85.7% [80.7, 89.6] (n=238) · 204/238 correctTargeted LoRA — Targeted LoRA seed 456: 85.3% [80.2, 89.2] (n=238) · 203/238 correctTargeted LoRA — Targeted LoRA seed 789: 83.6% [78.4, 87.8] (n=238) · 199/238 correctTargeted LoRA — Targeted LoRA seed 2024: 83.6% [78.4, 87.8] (n=238) · 199/238 correctFull LoRA — Full LoRA seed 42: 83.2% [77.9, 87.4] (n=238) · 198/238 correctFull LoRA — Full LoRA seed 123: 83.2% [77.9, 87.4] (n=238) · 198/238 correctFull LoRA — Full LoRA seed 456: 83.6% [78.4, 87.8] (n=238) · 199/238 correct
range-dot chart. 9 values across 3 series. Highest: Targeted LoRA at Targeted LoRA seed 123, 85.7%. Lowest: Full LoRA at Full LoRA seed 42, 83.2%. The full values are in the data table.
  • MedGemma-4B
  • Targeted LoRA
  • Full LoRA

Where the answer is committed: residual transplant flip rate by layer

1,396 detecting/missing residual-transplant pairs spanning 91 findings, MedGemma-4B. Only the four layer anchors stated in Chapter 5 are plotted (8% at 14, 35% at 15, 73% at 16, about 100% by 20); the curve is not interpolated.

0.01440.015.2080.016.40120.017.60160.018.80200.020xpercentAnswer-position transplant — x 14: 8.0% · anchors stated in the text; no dense per-layer trajectory file existsAnswer-position transplant — x 15: 35.0% · anchors stated in the text; no dense per-layer trajectory file existsAnswer-position transplant — x 16: 73.0% · median commit layer; 95% bootstrap CI [16, 16]Answer-position transplant — x 20: 100.0% · text states the curve reaches about 100% by layer 20Image-token control — x 14: 0.1% · upper bound: the control never exceeds 0.0007 (0.07%) at any layerImage-token control — x 15: 0.1% · upper bound: the control never exceeds 0.0007 (0.07%) at any layerImage-token control — x 16: 0.1% · upper bound: the control never exceeds 0.0007 (0.07%) at any layerImage-token control — x 20: 0.1% · upper bound: the control never exceeds 0.0007 (0.07%) at any layer
line chart. 8 values across 2 series. Highest: Answer-position transplant at 20, 100.0%. Lowest: Image-token control at 14, 0.1%. The full values are in the data table.
  • Answer-position transplant
  • Image-token control

The diagnosis layer is not the best intervention layer

Five LoRA layer-range configurations (rank 16, alpha=32, combined loss, lambda=1.0) scored on the 355-question MIMIC-CXR validation split against the 1.87 no-adapter baseline.

0.0000.6001.201.802.403.00Baseline (no LoRA)Early (0-10)Random block (5-9)All (0-33)Middle (15-19)Late (25-33)logit margin differenceNo adapter — Baseline (no LoRA): 1.87 (n=355) · reference margin differenceLoRA layer window — Early (0-10): 0.260 (n=355) · 86% reduction from the 1.87 baselineLoRA layer window — Random block (5-9): 0.300 (n=355) · 84% reductionLoRA layer window — All (0-33): 0.340 (n=355) · 82% reductionLoRA layer window — Middle (15-19): 0.380 (n=355) · 80% reduction; the mechanistically identified windowLoRA layer window — Late (25-33): 0.700 (n=355) · 63% reduction
bar chart. 6 values across 2 series. Highest: No adapter at Baseline (no LoRA), 1.87. Lowest: LoRA layer window at Early (0-10), 0.260. The full values are in the data table.
  • No adapter
  • LoRA layer window

Exploratory: layer-17 sparse-autoencoder features most associated with flipping

EXPLORATORY. Top 8 GemmaScope 2 features at layer 17 by absolute rank-biserial association with flipping, Targeted LoRA on the 861-question PadChest flip bank (5,112 records, 305 flipped questions).

0.0000.0800.1600.2400.3200.400#3818#21#534#2882#6871#4103#12762#2913rank-biserial correlationLayer 17 feature — #3818: -0.462 (n=861) · Feature 3818 — the candidate operator/register gate; prevalence 0.85Layer 17 feature — #21: -0.393 (n=861) · prevalence 0.61Layer 17 feature — #534: -0.335 (n=861) · prevalence 1.00Layer 17 feature — #2882: -0.321 (n=861) · prevalence 0.74Layer 17 feature — #6871: -0.313 (n=861) · prevalence 0.86Layer 17 feature — #4103: -0.301 (n=861) · prevalence 0.34Layer 17 feature — #12762: 0.290 (n=861) · prevalence 0.98Layer 17 feature — #2913: 0.263 (n=861) · prevalence 0.42
bar chart. 8 values across 1 series. Highest: Layer 17 feature at #12762, 0.290. Lowest: Layer 17 feature at #3818, -0.462. The full values are in the data table.

Predictive entropy of stable versus flipped predictions

Targeted LoRA on the 861-question PadChest flip bank: 747 stable questions and 114 that flipped under paraphrase.

00.02200.15400.28600.41800.541000.68Predictive entropy (nats)QuestionsStable — entropy ≈ 0.191: 10 questionsStable — entropy ≈ 0.225: 32 questionsStable — entropy ≈ 0.26: 55 questionsFlipped — entropy ≈ 0.26: 2 questionsStable — entropy ≈ 0.295: 84 questionsFlipped — entropy ≈ 0.295: 1 questionsStable — entropy ≈ 0.329: 80 questionsFlipped — entropy ≈ 0.329: 4 questionsStable — entropy ≈ 0.364: 81 questionsFlipped — entropy ≈ 0.364: 4 questionsStable — entropy ≈ 0.399: 53 questionsStable — entropy ≈ 0.433: 83 questionsFlipped — entropy ≈ 0.433: 7 questionsStable — entropy ≈ 0.468: 43 questionsFlipped — entropy ≈ 0.468: 10 questionsStable — entropy ≈ 0.503: 57 questionsFlipped — entropy ≈ 0.503: 4 questionsStable — entropy ≈ 0.537: 25 questionsFlipped — entropy ≈ 0.537: 5 questionsStable — entropy ≈ 0.572: 32 questionsFlipped — entropy ≈ 0.572: 6 questionsStable — entropy ≈ 0.607: 35 questionsFlipped — entropy ≈ 0.607: 12 questionsStable — entropy ≈ 0.641: 28 questionsFlipped — entropy ≈ 0.641: 10 questionsStable — entropy ≈ 0.676: 49 questionsFlipped — entropy ≈ 0.676: 49 questions
histogram chart. 40 values across 2 series. Highest: Stable at 0.295, 84.0. Lowest: Stable at 0.017, 0.000. The full values are in the data table.
  • Stable
  • Flipped

Predicting a paraphrase flip from uncertainty on the original question

Targeted LoRA on the 861-question PadChest flip bank (temperature scaling on the 732-question subset that survives its 15% calibration holdout). AUROC for predicting whether the question will flip.

0.0000.2000.4000.6000.8001.00Softmax entropyMonte Carlo dropoutDeep ensembleTemperature scalingAbsolute marginAUROCTargeted LoRA, PadChest — Softmax entropy: 0.823 (n=861) · p = 4.3e-29Targeted LoRA, PadChest — Monte Carlo dropout: 0.823 (n=861) · p = 4.6e-29Targeted LoRA, PadChest — Deep ensemble: 0.552 (n=861) · p = 3.7e-2; the only genuinely distinct signal, and it failsTargeted LoRA, PadChest — Temperature scaling: 0.813 (n=732) · n=732: temperature is fitted on a 15% calibration holdout, so 129 rows are withheldTargeted LoRA, PadChest — Absolute margin: 0.823 (n=861) · rank-equivalent to softmax entropy
bar chart. 5 values across 1 series. Highest: Targeted LoRA, PadChest at Softmax entropy, 0.823. Lowest: Targeted LoRA, PadChest at Deep ensemble, 0.552. The full values are in the data table.

Risk-coverage: what accepting fewer predictions buys you

PadChest flip bank, n=861 questions per model. Selective risk (error rate among accepted) against coverage, ranking by softmax confidence, for base MedGemma-4B, Targeted LoRA and Full LoRA. Curves recomputed from the post-image-fix run and downsampled to 50 points each.

0.0000.000.4000.200.8000.401.200.601.600.802.001.00xselective risk (error rate on accepted)
line chart. 150 values across 3 series. Highest: Full LoRA at 0.0012, 1.00. Lowest: MedGemma-4B at 0.0012, 0.000. The full values are in the data table.
  • MedGemma-4B
  • Targeted LoRA
  • Full LoRA

Offline readiness audit: admission rate against accuracy

Six cells: base MedGemma-4B, Targeted LoRA and Full LoRA on the MIMIC-CXR flip bank (n=98) and the PadChest flip bank (n=861). The audit admits a prediction only if the first two paraphrases agree with the original and the answer shows image reliance.

0.040.080.0120.0160.0200.0Base MedGemma-4B / MIMIC-CXRBase MedGemma-4B / PadChestTargeted LoRA / MIMIC-CXRTargeted LoRA / PadChestFull LoRA / MIMIC-CXRFull LoRA / PadChestpercent (%)Admitted by the audit — Base MedGemma-4B / MIMIC-CXR: 22.4% (n=98) · 22 of 98 questions admittedAccuracy on admitted — Base MedGemma-4B / MIMIC-CXR: 95.5% (n=98)Accuracy on all questions — Base MedGemma-4B / MIMIC-CXR: 83.7% (n=98) · residual flip on uninspected paraphrases: 40.9%Admitted by the audit — Base MedGemma-4B / PadChest: 41.6% (n=861) · 358 of 861 questions admittedAccuracy on admitted — Base MedGemma-4B / PadChest: 96.9% (n=861)Accuracy on all questions — Base MedGemma-4B / PadChest: 78.6% (n=861) · residual flip on uninspected paraphrases: 72.9%Admitted by the audit — Targeted LoRA / MIMIC-CXR: 30.6% (n=98) · 30 of 98 questions admittedAccuracy on admitted — Targeted LoRA / MIMIC-CXR: 76.7% (n=98)Accuracy on all questions — Targeted LoRA / MIMIC-CXR: 77.6% (n=98) · residual flip on uninspected paraphrases: 23.3%Admitted by the audit — Targeted LoRA / PadChest: 33.0% (n=861) · 284 of 861 questions admittedAccuracy on admitted — Targeted LoRA / PadChest: 96.8% (n=861)Accuracy on all questions — Targeted LoRA / PadChest: 91.5% (n=861) · residual flip on uninspected paraphrases: 4.2%Admitted by the audit — Full LoRA / MIMIC-CXR: 14.3% (n=98) · 14 of 98 questions admittedAccuracy on admitted — Full LoRA / MIMIC-CXR: 92.9% (n=98)Accuracy on all questions — Full LoRA / MIMIC-CXR: 81.6% (n=98) · residual flip on uninspected paraphrases: 0.0%Admitted by the audit — Full LoRA / PadChest: 26.7% (n=861) · 230 of 861 questions admittedAccuracy on admitted — Full LoRA / PadChest: 83.9% (n=861)Accuracy on all questions — Full LoRA / PadChest: 66.0% (n=861) · residual flip on uninspected paraphrases: 10.4%
grouped-bar chart. 18 values across 3 series. Highest: Accuracy on admitted at Base MedGemma-4B / PadChest, 96.9%. Lowest: Admitted by the audit at Full LoRA / MIMIC-CXR, 14.3%. The full values are in the data table.
  • Admitted by the audit
  • Accuracy on admitted
  • Accuracy on all questions

How many generated paraphrases survive the equivalence audit

All 122,778 candidate pairs adjudicated by the rubric audit: MIMIC-CXR 12,259, PadChest 79,378, VinDr-CXR 31,141. Overall 61,761 retained (50.3%), 59,788 rejected (48.7%), 1,229 uncertain (1.0%).

0.00020.040.060.080.0100.0MIMIC-CXRPadChestVinDr-CXRparaphrase pairsRetained as equivalent, MIMIC-CXR: 8933.0Rejected as not equivalent, MIMIC-CXR: 3113.0Uncertain, MIMIC-CXR: 213.0Retained as equivalent, PadChest: 28364.0Rejected as not equivalent, PadChest: 50200.0Uncertain, PadChest: 814.0Retained as equivalent, VinDr-CXR: 24464.0Rejected as not equivalent, VinDr-CXR: 6475.0Uncertain, VinDr-CXR: 202.0
stacked-bar chart. 9 values across 3 series. Highest: Rejected as not equivalent at PadChest, 50200.0. Lowest: Uncertain at VinDr-CXR, 202.0. The full values are in the data table.
  • Retained as equivalent
  • Rejected as not equivalent
  • Uncertain

Which kinds of rephrasing break the model

Mean pairwise flip rate over the six base models on the PSF-Med binary yes/no subset, by transformation type. Per-cell n is binary-subset pairs per model (11 to 14,662). PadChest has no negation cell.

0.08.016.024.032.040.0Lexical substitutionSyntactic restructuringScope quantificationSpecificity modulationNegation patternpercent (%)MIMIC-CXR — Lexical substitution: 11.1% (n=1,511)PadChest — Lexical substitution: 17.2% (n=14,662)VinDr-CXR — Lexical substitution: 12.5% (n=2,816)MIMIC-CXR — Syntactic restructuring: 8.7% (n=1,400)PadChest — Syntactic restructuring: 16.5% (n=8,235)VinDr-CXR — Syntactic restructuring: 18.6% (n=1,145)MIMIC-CXR — Scope quantification: 9.8% (n=1,183)PadChest — Scope quantification: 17.2% (n=8,095)VinDr-CXR — Scope quantification: 14.1% (n=1,842)MIMIC-CXR — Specificity modulation: 10.0% (n=971)PadChest — Specificity modulation: 5.3% (n=5,252)VinDr-CXR — Specificity modulation: 14.3% (n=1,715)MIMIC-CXR — Negation pattern: 18.2% (n=11) · only 11 pairs survive the audit on MIMIC-CXR; reported for completeness onlyVinDr-CXR — Negation pattern: 34.7% (n=1,126) · highest cell in the table; the VinDr regeneration is the only source with a usable negation pool
grouped-bar chart. 14 values across 3 series. Highest: VinDr-CXR at Negation pattern, 34.7%. Lowest: PadChest at Specificity modulation, 5.3%. The full values are in the data table.
  • MIMIC-CXR
  • PadChest
  • VinDr-CXR

Clinicians agree with each other; the automated judge is the weak link

1,200-pair stratified adjudication sample, three reviewers, 400 pairs triple-reviewed. Reviewer-versus-reviewer agreement on the 400; clinician-consensus-versus-GPT-5-mini agreement on all 1,200 and on the 400.

0.040.080.0120.0160.0200.0Three clinician pairs, triple-reviewed subset (n=400)Full adjudication sample (n=1,200)Triple-reviewed subset (n=400)percent (%)Reviewer against reviewer — Three clinician pairs, triple-reviewed subset (n=400): 98.5% (n=400) · Cohen's kappa 0.97; all three pairs report the same 98.5% / 0.97Clinician consensus against the automated judge — Full adjudication sample (n=1,200): 72.3% (n=1,200) · Cohen's kappa 0.52; 868 of 1,200 pairsClinician consensus against the automated judge — Triple-reviewed subset (n=400): 78.2% (n=400) · 313 of 400; the subset where the consensus label is best supported
bar chart. 3 values across 2 series. Highest: Reviewer against reviewer at Three clinician pairs, triple-reviewed subset (n=400), 98.5%. Lowest: Clinician consensus against the automated judge at Full adjudication sample (n=1,200), 72.3%. The full values are in the data table.
  • Reviewer against reviewer
  • Clinician consensus against the automated judge

Accuracy by patient age band

PadChest flip bank only, n=861 questions per model split into four age bands (under 40 n=48, 40-60 n=233, 60-80 n=416, over 80 n=164), for base MedGemma-4B, Targeted LoRA and Full LoRA.

0.040.080.0120.0160.0200.0Under 4040 to 6060 to 80Over 80percent (%)MedGemma-4B — Under 40: 79.2% (n=48) · smallest bin in the analysisTargeted LoRA — Under 40: 83.3% (n=48) · smallest bin in the analysisFull LoRA — Under 40: 66.7% (n=48) · smallest bin in the analysisMedGemma-4B — 40 to 60: 76.8% (n=233)Targeted LoRA — 40 to 60: 87.6% (n=233)Full LoRA — 40 to 60: 63.1% (n=233)MedGemma-4B — 60 to 80: 78.8% (n=416)Targeted LoRA — 60 to 80: 92.5% (n=416)Full LoRA — 60 to 80: 65.9% (n=416)MedGemma-4B — Over 80: 81.1% (n=164)Targeted LoRA — Over 80: 96.3% (n=164)Full LoRA — Over 80: 71.3% (n=164)
grouped-bar chart. 12 values across 3 series. Highest: Targeted LoRA at Over 80, 96.3%. Lowest: Full LoRA at 40 to 60, 63.1%. The full values are in the data table.
  • MedGemma-4B
  • Targeted LoRA
  • Full LoRA

Does failure detection survive image corruption?

AUGRC against corruption severity (0 = clean, then 1/3/5 averaged over five corruption types) on the 861-question PadChest flip bank, softmax entropy, for base MedGemma-4B, Targeted LoRA and Full LoRA. Bars are bootstrap 95% intervals. Lower is better.

0.00000.06010.12020.18030.24040.3005xAUGRCMedGemma-4B — x 0: 0.047 · clean, uncorrupted imagesMedGemma-4B — x 1: 0.049 · mean over 5 corruption typesMedGemma-4B — x 3: 0.059 · mean over 5 corruption typesMedGemma-4B — x 5: 0.088 · mean over 5 corruption typesTargeted LoRA — x 0: 0.014 · clean, uncorrupted imagesTargeted LoRA — x 1: 0.015 · mean over 5 corruption typesTargeted LoRA — x 3: 0.018 · mean over 5 corruption typesTargeted LoRA — x 5: 0.027 · mean over 5 corruption typesFull LoRA — x 0: 0.153 · clean, uncorrupted imagesFull LoRA — x 1: 0.154 · mean over 5 corruption typesFull LoRA — x 3: 0.162 · mean over 5 corruption typesFull LoRA — x 5: 0.185 · mean over 5 corruption types
line chart. 12 values across 3 series. Highest: Full LoRA at 5, 0.185. Lowest: Targeted LoRA at 0, 0.014. The full values are in the data table.
  • MedGemma-4B
  • Targeted LoRA
  • Full LoRA

6.4% to 54.7%

pairwise

Paraphrase flip rates span 6.4% to 54.7% across six medical VLMs

Rephrasing a clinical yes/no question in a way that preserves its meaning changes the answer on anywhere from 1 in 16 to more than half of paraphrase pairs, depending on which model and which patient population you test. Paraphrase sensitivity is not a quirk of one weak model: every model tested flips, and the spread between the best and worst model-dataset cell is 8.5-fold.

Multiple models · Multiple datasets · A range over 18 model-dataset cells, so it has no single denominator. Per-dataset binary-subset denominators are MIMIC-CXR 1,539 questions / 5,076 pairs, PadChest 8,445 / 36,244, and VinDr-CXR 2,807 / 8,612, each evaluated on every model. The endpoints are MedGemma-27B on MIMIC (6.4%) and RadFM on VinDr (54.7%).

Source, denominator, and limits
How far this goesTwo of the 18 cells are set aside as degenerate yes-bias and excluded from this range: LLaVA-Rad on PadChest (99.6% positive predictions on originals) and MedGemma-1.5-4B on VinDr (100%). Both post a near-zero flip rate (0.8%) that reflects a single-class prior rather than paraphrase stability, so a model that answers 'yes' to everything would look maximally consistent here. The range also confounds model and population: it spans three different countries' patient distributions, so a cell's position reflects both the model and where the data came from.
Metric
pairwise paraphrase flip rate (range across six models x three datasets)
Denominator
pairwise (per question-paraphrase pair)
Sample
A range over 18 model-dataset cells, so it has no single denominator. Per-dataset binary-subset denominators are MIMIC-CXR 1,539 questions / 5,076 pairs, PadChest 8,445 / 36,244, and VinDr-CXR 2,807 / 8,612, each evaluated on every model. The endpoints are MedGemma-27B on MIMIC (6.4%) and RadFM on VinDr (54.7%).
Model
Multiple models
Dataset
Multiple datasets
Split
eval
Comparison
An 8.5x spread between the most consistent cell (MedGemma-27B on MIMIC) and the least consistent (RadFM on VinDr).
Uncertainty
not reported for this value
Thesis
Chapter 3, Table 3.3
Source artifact
results/uai/revision/psf_binary_recompute_v2.json
Last verified
2026-07-15

8.3%

pairwise

MedGemma-4B flips on 8.3% of MIMIC-CXR paraphrase pairs

On in-distribution US chest X-ray questions, the mechanistic subject model gives a different yes/no answer to about 1 in 12 clinically equivalent rephrasings.

MedGemma-4B · MIMIC-CXR · n = 5,076

Source, denominator, and limits
How far this goesThe MIMIC binary subset is not a pure presence set: it mixes 956 presence questions, 177 view-identification questions and 406 abnormality questions, so the rate averages over question types that may not be equally fragile. This is the pairwise denominator (flipped paraphrases over paraphrase pairs); the query-level rate on the same questions is more than double at 18.1%, and the two must never be interchanged. Do not compare this figure to the 42.1% MedGemma-4B flip rate on the 107-pair diagnostic set of Chapter 4, which is a different, deliberately hard population.
Metric
pairwise paraphrase flip rate
Denominator
pairwise (per question-paraphrase pair)
Sample
n = 5,076
Model
MedGemma-4B
Dataset
MIMIC-CXR
Split
eval
Comparison
13.4% for the same model on PadChest and 15.3% on VinDr-CXR; 18.1% query-level on this same MIMIC set.
Uncertainty
not reported for this value
Thesis
Chapter 3, Table 3.3
Source artifact
results/uai/revision/psf_binary_recompute_v2.json
Last verified
2026-07-15

13.4%

pairwise

MedGemma-4B flips on 13.4% of PadChest paraphrase pairs

On Spanish chest X-ray questions, out of distribution for a model trained on US data, MedGemma-4B changes its yes/no answer on about 1 in 7 equivalent rephrasings, noticeably more often than on MIMIC.

MedGemma-4B · PadChest · n = 36,244

Source, denominator, and limits
How far this goesPadChest is 83% positive by reference while the model predicts positive only 24% of the time, so this set measures phrasing stability and cannot support balanced-accuracy or sensitivity/specificity claims. The obsolete pre-audit value of 42.4% for this cell (reported in an earlier unfiltered pre-audit run) was inflated by adversarial reframing pairs and is not a current result. The near-identical 13.13% figure that appears in the full-question PadChest v2 run uses a different denominator (all 14,935 questions, not the 8,445-question binary subset) and should not be substituted.
Metric
pairwise paraphrase flip rate
Denominator
pairwise (per question-paraphrase pair)
Sample
n = 36,244
Model
MedGemma-4B
Dataset
PadChest
Split
ood
Comparison
8.3% for the same model on MIMIC-CXR; 32.2% query-level on this same PadChest set.
Uncertainty
not reported for this value
Thesis
Chapter 3, Table 3.3
Source artifact
results/uai/revision/psf_binary_recompute_v2.json
Last verified
2026-07-15

32.2%

query-level

Query-level flip rate is 32.2% where the pairwise rate is 13.4%

Counting questions instead of pairs more than doubles the apparent failure rate on identical data. Asking 'how often does one rephrasing change the answer?' gives 13.4%; asking 'how many questions break under at least one of their roughly 3.3 rephrasings?' gives 32.2%. Neither is wrong, but a benchmark that does not say which one it reports is uninterpretable.

MedGemma-4B · PadChest · n = 8,445

Source, denominator, and limits
How far this goesThe two denominators answer different questions and must never be mixed or averaged. The query-level rate rises mechanically with the number of paraphrases per question, so it is only comparable across sets with the same paraphrase depth; the gap between 13.4% and 32.2% is largely a property of having roughly 3.3 chances to fail per question, not evidence of a second failure mode. Every flip rate on this site is tagged with its denominator for this reason. The later v2 recompute gives 32.1% for this cell, a 0.15 percentage-point drift from the value the thesis table reports.
Metric
query-level paraphrase flip rate (fraction of questions flipping on at least one paraphrase)
Denominator
query-level (per question)
Sample
n = 8,445
Model
MedGemma-4B
Dataset
PadChest
Split
ood
Comparison
13.4% pairwise on the same questions
Uncertainty
not reported for this value
Thesis
Chapter 3, Table 3.4
Source artifact
results/uai/revision/psf_binary_recompute.json
Last verified
2026-07-15

92,856

pairwise

PSF-Med evaluates 92,856 question-paraphrase pairs

The benchmark that every headline flip rate is computed on contains 92,856 (question, paraphrase) pairs across three continents: 8,938 from MIMIC-CXR, 59,573 from PadChest v2 and 24,345 from VinDr-CXR, averaging 3.5 paraphrases per question.

Not model-specific · Multiple datasets · n = 92,856

Source, denominator, and limits
How far this goes92,856 is NOT the same thing as the roughly 92,000 candidate paraphrases generated at construction time; the two numbers are close by coincidence and describe different pipeline stages. The construction stage produced about 92,000 candidates (mean 4.7 per question) before filtering; 92,856 is the post-audit evaluation set (mean 3.5 per question). It is also not a subset of the 61,761 audit-retained core, because the audited PadChest v1 pairs were superseded by a separately filtered PadChest v2 set. Sources that report 'about 92,000 pairs' as the evaluation size are blurring two stages.
Metric
post-audit evaluation pairs in the PSF-Med benchmark
Denominator
pairwise (per question-paraphrase pair)
Sample
n = 92,856
Model
Not model-specific
Dataset
Multiple datasets
Split
eval
Comparison
Distinct from the roughly 92,000 construction-stage candidate paraphrases and from the 122,778 pairs sent through the rubric audit.
Uncertainty
not reported for this value
Thesis
Chapter 3, Table 3.1
Source artifact
dissertation/tables/thrust1/table_dataset_stats.tex
Last verified
2026-07-15

26,850

question-level

PSF-Med covers 26,850 original clinical questions

The benchmark is built from 26,850 original chest X-ray questions drawn from three healthcare systems: 3,266 from MIMIC-CXR (United States), 14,935 from PadChest (Spain) and 8,649 from VinDr-CXR (Vietnam).

Not model-specific · Multiple datasets · n = 26,850

Source, denominator, and limits
How far this goesThis is the full question pool, not the population the headline flip rates run on. Flip rates are reported only on the binary yes/no subset for which a yes/no readout is defined: 1,539 MIMIC, 8,445 PadChest and 2,807 VinDr questions, or about half the pool. Location and laterality questions and nine VinDr non-binary references are excluded. The pool is also heavily weighted toward PadChest (56% of questions), so any statistic pooled across datasets is dominated by one Spanish hospital.
Metric
original clinical questions in the PSF-Med benchmark
Denominator
question-level (per question)
Sample
n = 26,850
Model
Not model-specific
Dataset
Multiple datasets
Split
eval
Comparison
Expands to 92,856 evaluation pairs at a mean of 3.5 paraphrases per question.
Uncertainty
not reported for this value
Thesis
Chapter 3, Table 3.1
Source artifact
dissertation/tables/thrust1/table_dataset_stats.tex
Last verified
2026-07-15

50.3%

pairwise

The equivalence audit rejects nearly half of generated paraphrases

A rubric-based GPT-5-mini audit of 122,778 candidate pairs kept only 50.3% (61,761) as clinically equivalent, rejected 48.7% (59,788) as adversarial or meaning-changing, and flagged 1.0% (1,229) as uncertain. Roughly half of what an LLM generates as a 'paraphrase' of a clinical question does not preserve the question.

Not model-specific · Multiple datasets · n = 122,778

Source, denominator, and limits
How far this goesThe 92,856-pair evaluation set is NOT a subset of the 61,761-pair retained core: the audit's retained PadChest v1 pairs were superseded by a regenerated, separately judge-filtered PadChest v2 set (59,573 pairs), which combined with audit-retained MIMIC (8,938) and VinDr (24,345) gives the 92,856 evaluated pairs. So the 50.3% describes the audit population, not the shipped benchmark. These tallies supersede an earlier snapshot of the same audit (60,712 retained / 60,766 rejected / 1,300 uncertain) taken while the VinDr run was still completing. Retention is a judge verdict, not a clinician verdict; the clinician sample agrees with this judge only 72.3% of the time.
Metric
share of audited paraphrase pairs retained as core-equivalent
Denominator
pairwise (per question-paraphrase pair)
Sample
n = 122,778
Model
Not model-specific
Dataset
Multiple datasets
Split
eval
Comparison
Retention varies sharply by dataset: 72.9% MIMIC, 35.7% PadChest, 78.6% VinDr.
Uncertainty
not reported for this value
Thesis
Chapter 3, Chapter 3, Rubric-Based Equivalence Audit
Source artifact
scripts/analysis/recompute_audit_retention.py
Last verified
2026-07-15

72.3%

pairwise

The automated judge agrees with clinicians on only 72.3% of pairs

Three reviewers (a radiologist, a clinician, and a device-research reviewer) adjudicated a 1,200-pair sample and agreed with each other almost perfectly (98.5% raw, kappa 0.97 on the 400 triple-reviewed pairs), but the GPT-5-mini equivalence judge matched their consensus on just 72.3% of pairs (kappa 0.52). The judge and the clinicians agree on easy cases and diverge exactly on the adversarial and uncertain ones the sample was designed to over-represent.

Not model-specific · Multiple datasets · n = 1,200 · Cohen's kappa 0.52 (moderate); consensus labels 689 equivalent / 409 not equivalent / 102 uncertain

Source, denominator, and limits
How far this goesThis validates the judge, NOT the benchmark's flip rates. The 1,200-pair sample was drawn before the PadChest v2 regeneration, so only 95 of 174 MIMIC pairs, 45 of 658 PadChest pairs and 0 of 43 VinDr pairs are in the evaluated benchmark at all. It therefore cannot be used to restate any flip rate; on the 95 overlapping MIMIC pairs, restricting to clinician-confirmed equivalents moves per-model flip rates by only -2.5 to +1.1 percentage points, within sampling noise. An earlier '0.9 pp sensitivity' claim from this sample was unreproducible and has been removed. The sample also deliberately over-samples adversarial and uncertain pairs, so 72.3% is a lower bound on agreement over a natural pair mix, not an unbiased estimate.
Metric
raw agreement between clinician consensus and the GPT-5-mini equivalence judge
Denominator
pairwise (per question-paraphrase pair)
Sample
n = 1,200
Model
Not model-specific
Dataset
Multiple datasets
Split
eval
Comparison
Inter-reviewer agreement on the same task is 98.5% raw (394/400, Cohen's kappa 0.97); judge-vs-judge cross-family agreement is 91.6-94.4%.
Uncertainty
Cohen's kappa 0.52 (moderate); consensus labels 689 equivalent / 409 not equivalent / 102 uncertain
Thesis
Chapter 3, Chapter 3, Clinician Adjudication
Source artifact
results/clinician_review/run_v2/kappa_report.json
Last verified
2026-07-15

91.6% to 94.4%

pairwise

Two LLM judge families agree with each other on 91.6% to 94.4% of pairs

Re-scoring paraphrase pairs with Claude Haiku instead of GPT-5-mini reproduces the equivalence verdict 91.6% to 94.4% of the time, so the audit's filtering is not an artifact of one model family's idiosyncrasies. Disagreements concentrate in scope and quantification cases.

Not model-specific · Multiple datasets · Reported as a range across judge runs without a single pooled denominator. The dissertation states 91.6% on a stratified 500-pair re-scoring subset covering MIMIC + PadChest; the 94.4% upper end is quoted as the top of the cross-family range and its denominator is not separately stated in the source.

Source, denominator, and limits
How far this goesHigh agreement between two LLM judges is evidence of reproducibility, not of correctness: both families can share the same blind spot. The decisive comparison is the clinician one, where the same judge agrees only 72.3% of the time (kappa 0.52). Two LLMs agreeing 9 times in 10 while agreeing with doctors 7 times in 10 means cross-family agreement should not be read as clinical validation of the filter.
Metric
cross-family agreement between the GPT-5-mini judge and a Claude Haiku re-scoring
Denominator
pairwise (per question-paraphrase pair)
Sample
Reported as a range across judge runs without a single pooled denominator. The dissertation states 91.6% on a stratified 500-pair re-scoring subset covering MIMIC + PadChest; the 94.4% upper end is quoted as the top of the cross-family range and its denominator is not separately stated in the source.
Model
Not model-specific
Dataset
Multiple datasets
Split
eval
Comparison
Far higher than the 72.3% agreement between this judge and clinician consensus.
Uncertainty
not reported for this value
Thesis
Chapter 3, Chapter 3, Rubric-Based Equivalence Audit
Source artifact
dissertation/chapters/02_thrust1.tex
Last verified
2026-07-15

93.3%

pairwise

The audit rejects 93.3% of generated negation paraphrases

Negation rewrites are almost never valid paraphrases: 93.3% were rejected by the audit as polarity- or operator-changing, meaning the rewritten question has a different correct answer. An earlier headline claim that negation was the most fragile transformation type largely dissolves once these are removed, because the model was being marked wrong for correctly noticing that the question had changed.

Not model-specific · Multiple datasets · The source reports the rejection rate for the negation-pattern category without stating the category's pre-audit denominator. The surviving counts are given instead: 11 negation pairs per model on MIMIC, almost none on PadChest, and roughly 1,126 on VinDr.

Source, denominator, and limits
How far this goesThis rejection is a judge verdict on generated candidates, so it measures how badly LLM paraphrase generation handles negation, not a property of the models under test. It leaves the surviving negation category too thin to estimate on two of three datasets (11 pairs per model on MIMIC, almost none on PadChest), so the 34.7% VinDr negation flip rate rests on one dataset. The same operator confound persists downstream: 39% of the flips in the canonical mechanistic screen are still negation-pattern operator changes, which inflates the SAE restoration and mediation results.
Metric
share of generated negation-pattern paraphrases rejected by the rubric equivalence audit
Denominator
pairwise (per question-paraphrase pair)
Sample
The source reports the rejection rate for the negation-pattern category without stating the category's pre-audit denominator. The surviving counts are given instead: 11 negation pairs per model on MIMIC, almost none on PadChest, and roughly 1,126 on VinDr.
Model
Not model-specific
Dataset
Multiple datasets
Split
eval
Comparison
Lexical substitutions were retained most often; overall retention across all transformation types was 50.3%.
Uncertainty
not reported for this value
Thesis
Chapter 3, Table 3.5
Source artifact
dissertation/chapters/02_thrust1.tex
Last verified
2026-07-15

6.4%

pairwise

Scaling from 4B to 27B does not reliably buy paraphrase consistency

MedGemma-27B is the most consistent model on MIMIC at 6.4%, beating the 4B variant's 8.3%, but the advantage does not hold out of distribution: on PadChest the 27B model is marginally worse (13.9% vs 13.4%). Six times the parameters produces a small in-distribution gain that reverses on a different population, so scale is not a fix for paraphrase sensitivity.

MedGemma-27B · MIMIC-CXR · n = 5,076

Source, denominator, and limits
How far this goesThis is a within-family comparison on MedGemma only (27B vs 4B vs 1.5-4B); it says nothing about scaling in other architectures, and the six-model benchmark shows no stable ordering by parameter count overall (the 14B RadFM is the worst model on every dataset). The PadChest reversal (27B 13.9% vs 4B 13.4%) is small and reported without a confidence interval, so it is better read as 'no reliable advantage out of distribution' than as evidence that scale actively hurts. The two models also differ in training data, not only size, so this is not a clean scaling experiment.
Metric
pairwise paraphrase flip rate
Denominator
pairwise (per question-paraphrase pair)
Sample
n = 5,076
Model
MedGemma-27B
Dataset
MIMIC-CXR
Split
eval
Comparison
8.3% for MedGemma-4B on the same set; the ordering reverses on PadChest, where the 27B model flips on 13.9% of pairs against the 4B model's 13.4%.
Uncertainty
not reported for this value
Thesis
Chapter 3, Table 3.3
Source artifact
results/uai/revision/psf_binary_recompute_v2.json
Last verified
2026-07-15

96.3%

pairwise

LLaVA-Rad gives the same answer 96.3% of the time with the image removed

Delete the chest X-ray entirely and ask LLaVA-Rad the same question, and it returns its original answer on 96 of every 100 pairs. It is also the most paraphrase-consistent of the three backends tested here (6.5% flip rate). Its consistency is almost entirely a property of the question text: the image is nearly decorative.

LLaVA-Rad · Multiple datasets · n = 107

Source, denominator, and limits
How far this goesThe 107-pair diagnostic set is NOT comparable to the main benchmark: MedGemma-4B flips on 42.1% of these pairs against 8.3% on the benchmark, roughly 5x, because the set was curated for semantic closeness on a small pool. Read these numbers as a within-set contrast between backends, never against a benchmark flip rate. The set also predates the equivalence audit, so some pairs are operator changes rather than true paraphrases. Text-only agreement is a behavioural screen, not a per-prediction verdict: a model can agree with itself without the image and still be right, because base rates make the text prior usually correct.
Metric
text-only agreement (share of predictions unchanged when the image is removed)
Denominator
pairwise (per question-paraphrase pair)
Sample
n = 107
Model
LLaVA-Rad
Dataset
Multiple datasets
Split
eval
Comparison
66.4% for MedGemma-4B and 85.0% for MedGemma-27B on the same 107 pairs; LLaVA-Rad's flip rate on this set is the lowest of the three (6.5% vs 42.1% for MedGemma-4B).
Uncertainty
not reported for this value
Thesis
Chapter 4, tab:thrust2_backend_summary
Paper
Source artifact
dissertation/tables/thrust2/table_backend_summary.tex
Last verified
2026-07-15

66.4%

pairwise

MedGemma-4B is the most image-dependent backend at 66.4% text-only agreement

MedGemma-4B changes its answer on a third of pairs when the image is taken away, far more than LLaVA-Rad (96.3% agreement) or MedGemma-27B (85.0%). It is the most image-dependent of the three backends, and it is also the most paraphrase-fragile. Being harder to fool with a missing image goes together with being easier to shake with a rephrasing.

MedGemma-4B · Multiple datasets · n = 107

Source, denominator, and limits
How far this goesThe 107-pair diagnostic set is NOT comparable to the main benchmark: MedGemma-4B flips on 42.1% of these pairs against 8.3% on the benchmark, roughly 5x. Do not read the 42.1% as a benchmark result or compare this text-only rate to rates measured on other evaluation sets. The set predates the equivalence audit, so some pairs are operator changes. Lower text-only agreement means less image-invariance, which is desirable, but it does not establish that the model uses the image *correctly*: MedGemma-4B also has the worst robust accuracy of the three backends here (36.4%).
Metric
text-only agreement (share of predictions unchanged when the image is removed)
Denominator
pairwise (per question-paraphrase pair)
Sample
n = 107
Model
MedGemma-4B
Dataset
Multiple datasets
Split
eval
Comparison
96.3% for LLaVA-Rad and 85.0% for MedGemma-27B on the same 107 pairs.
Uncertainty
not reported for this value
Thesis
Chapter 4, tab:thrust2_backend_summary
Paper
Source artifact
dissertation/tables/thrust2/table_backend_summary.tex
Last verified
2026-07-15

30.8%

pairwise

MedGemma-4B changes its answer on 30.8% of pairs when given the wrong image

Swap in a different patient's chest X-ray and MedGemma-4B changes its answer about 31% of the time, the highest of the three backends. LLaVA-Rad barely notices (10.3%). This is the complement of the text-only test: a grounded model should react when the evidence changes.

MedGemma-4B · Multiple datasets · n = 107

Source, denominator, and limits
How far this goesThe 107-pair diagnostic set is NOT comparable to the main benchmark: MedGemma-4B flips on 42.1% of these pairs against 8.3% on the benchmark, roughly 5x, so no rate here transfers to a benchmark claim. Swap sensitivity has no correctness axis: a model that reacts to a swapped image is using the image, but the reaction can be in either direction and may be wrong. Even the most reactive backend here ignores the swap on 69% of pairs. The set predates the equivalence audit.
Metric
image-swap sensitivity (share of predictions that change when the image is replaced with a different patient's)
Denominator
pairwise (per question-paraphrase pair)
Sample
n = 107
Model
MedGemma-4B
Dataset
Multiple datasets
Split
eval
Comparison
19.6% for MedGemma-27B and 10.3% for LLaVA-Rad on the same 107 pairs.
Uncertainty
not reported for this value
Thesis
Chapter 4, tab:thrust2_backend_summary
Paper
Source artifact
dissertation/tables/thrust2/table_backend_summary.tex
Last verified
2026-07-15

-0.997

model-dataset

Across three backends, flip rate and text-only agreement are almost perfectly opposed

Ordering the three backends by paraphrase fragility exactly reverses their ordering by image-independence (Pearson r = -0.997): the model that flips least ignores the image most. This is the picture that motivates the whole thesis, but it rests on three points.

Multiple models · Multiple datasets · n = 3

Source, denominator, and limits
How far this goesThree points cannot establish a law. An r computed on n=3 carries almost no inferential content: almost any three monotone points yield |r| near 1, and a single additional model could destroy it. This is ILLUSTRATIVE of a tendency, not evidence of a deterministic trade-off, and it must never be reported as if it were fitted on the benchmark. The related r = -0.86 correlation in the safety chapter is separately known to be largely definitional, falling to r = -0.15 once conditioned on consistency. The 107-pair set is also not comparable to the main benchmark, where MedGemma-4B flips on 8.3% of pairs rather than 42.1%.
Metric
Pearson correlation between flip rate and text-only agreement across backends
Denominator
model-dataset (per model-dataset setting)
Sample
n = 3
Model
Multiple models
Dataset
Multiple datasets
Split
eval
Comparison
The same tension restated as a level on a larger 10-cell audit: 81% of consistent predictions are image-invariant (range 45-100%).
Uncertainty
not reported for this value
Thesis
Chapter 4, tab:thrust2_backend_summary
Paper
Source artifact
dissertation/tables/thrust2/table_backend_summary.tex
Last verified
2026-07-15

8.4%

case-level

Flipping cases put 41% less attention on the pathology region

When the model flips its answer under paraphrasing, it was already directing less attention into the radiologist-annotated pathology box: 8.4% of attention mass versus 14.2% for cases that stay consistent. Weak spatial grounding and paraphrase fragility travel together. But note the absolute level: even the consistent cases put only about one-seventh of their attention on the region that matters.

MedGemma-4B · PadChest · n = 200 · p < 1e-8, two-tailed t-test, t = -6.13

Source, denominator, and limits
How far this goesNo displaced-box or random-location control was run at this n=200, so the absolute coverage values are meaningless on their own: only the flip vs no-flip contrast is load-bearing. The larger 637-box grounding study in Chapter 6 shows why this matters, since attention there only marginally beats a deliberately shifted box, meaning it is a coarse localizer rather than a faithful one. The flip/no-flip split also uses pairs from before the equivalence audit, so some 'flip' cases are operator changes rather than paraphrases and the gap may be partly confounded by transformation type; the direction holds but the magnitude is suggestive. This chapter's diagnostic sets, including this one, are separate curated populations and are not comparable to the main benchmark, where MedGemma-4B flips on 8.3% of pairs against 42.1% on the companion 107-pair set.
Metric
ground-truth box attention coverage (share of attention mass inside the annotated pathology box)
Denominator
case-level (per case)
Sample
n = 200
Model
MedGemma-4B
Dataset
PadChest
Split
eval
Comparison
14.2% ground-truth coverage for no-flip cases on the same subsample, a 41% relative gap (p < 1e-8, two-tailed t-test, t = -6.13).
Uncertainty
p < 1e-8, two-tailed t-test, t = -6.13
Thesis
Chapter 4, tab:thrust2_attention_bbox
Paper
Source artifact
dissertation/chapters/03_thrust2.tex
Last verified
2026-07-15

3.5%

pairwise

Targeted LoRA cuts the flip rate from 8.5% to 3.5%, about 59%

Training a small adapter on five transformer layers cuts the paraphrase flip rate by roughly 59%, from 8.5% to 3.5%, on a held-out test that shares no patient and no image with training. The effect is significant in every one of five seeds.

Targeted LoRA · MIMIC-CXR · n = 238 · +/- 0.6 (standard deviation over five seeds)

Source, denominator, and limits
How far this goesDo not restate this as 79.5% (an old margin-difference reduction) or 69.6% (an older flip-rate reduction); on the clean patient-disjoint rerun the headline is about 59%. The base rate of 8.5% is itself lower than the 14.6% reported before the operator-preserving cleanup, because that earlier figure counted operator-changing pairs whose flips were correct behaviour. The split is disjoint by patient but NOT by institution: training and test are both MIMIC-CXR, so this does not demonstrate transfer to another hospital. Most importantly, this consistency gain does not come from better grounding, and Full LoRA reaches a slightly lower flip rate (2.9% +/- 0.3) at 7x the parameters. The displayed value is the mean over five seeds: an individual seed file differs (seed 42 alone gives 2.7% pairwise flip and 85.3% accuracy).
Metric
pairwise paraphrase flip rate, presence-of-finding endpoint
Denominator
pairwise (per question-paraphrase pair)
Sample
n = 238
Model
Targeted LoRA
Dataset
MIMIC-CXR
Split
patient-disjoint
Comparison
8.5% base pairwise flip
Uncertainty
+/- 0.6 (standard deviation over five seeds)
Thesis
Chapter 5, tab:thrust3_main_results
Source artifact
results/lora_fleet_patient_disjoint/eval_targeted_s{42,123,456,789,2024}.json (mean over five seeds)
Last verified
2026-07-15

0.25 pp

question-level

No observed accuracy reduction from the targeted adapter

Accuracy on the held-out presence endpoint moves from 84.5% to 84.7% (+/- 1.0), a paired per-question difference of +0.25 percentage points with a 95% interval of [-1.00, +1.51]. There is no observed accuracy reduction: the flip-rate gain does not come out of correctness.

Targeted LoRA · MIMIC-CXR · n = 238 · 95% t interval [-1.00, +1.51] percentage points across five seeds

Source, denominator, and limits
How far this goesThe interval excludes a large accuracy cost but does NOT establish exact parity: the data are consistent with anything from a one-point loss to a 1.5-point gain. This is why the wording is 'no observed accuracy reduction' rather than 'no accuracy cost' or 'accuracy preserved' — the study was never powered for an equivalence claim, and none is made. The endpoint is also a single presence-of-finding task on one institution's data; accuracy here is not a clinical performance claim. The displayed value is the mean over five seeds: an individual seed file differs (seed 42 alone gives 2.7% pairwise flip and 85.3% accuracy).
Metric
paired per-question accuracy difference (targeted LoRA minus base), presence-of-finding endpoint
Denominator
question-level (per question)
Sample
n = 238
Model
Targeted LoRA
Dataset
MIMIC-CXR
Split
patient-disjoint
Comparison
84.5% base accuracy to 84.7% +/- 1.0 adapted; Full LoRA instead gives up about 1.2 points (83.3% +/- 0.2).
Uncertainty
95% t interval [-1.00, +1.51] percentage points across five seeds
Thesis
Chapter 5, tab:thrust3_main_results
Source artifact
results/lora_fleet_patient_disjoint/eval_targeted_s{42,123,456,789,2024}.json (mean over five seeds)
Last verified
2026-07-15

0.10%

model-dataset

The targeted adapter trains 0.10% of the model's parameters

The whole intervention is 4.38M trainable parameters out of roughly 4.4 billion, adapters on layers 15-19 only (rank 16, alpha 32), with the vision encoder frozen. It trains in under 20 minutes on a single A100. Full LoRA touches all 34 layers for 30.5M parameters (0.72%) and buys no better outcome.

Targeted LoRA · Not dataset-specific · Not an evaluation statistic and so has no sample: it is a parameter count over a single model configuration (4.38M trainable adapter parameters against roughly 4.4B in MedGemma-4B). Adapters sit in attention (q, k, v, o) and MLP (gate, up, down) projections on layers 15-19.

Source, denominator, and limits
How far this goesCheapness is the actual argument for the targeted adapter, not mechanism. The layer choice was motivated by the SAE analysis pointing at layers 15-19, but the layer ablation shows early layers (0-10) reduce margin difference MORE (0.26 vs 0.38), so 0.10% is not evidence that the mechanistically indicated layers are the right ones. This same 0.1% footprint also bounds what the Monte Carlo dropout probe can see: perturbing 0.1% of the weights cannot detect epistemic uncertainty carried by the frozen 99.9%.
Metric
trainable parameter fraction (4.38M of about 4.4B)
Denominator
model-dataset (per model-dataset setting)
Sample
Not an evaluation statistic and so has no sample: it is a parameter count over a single model configuration (4.38M trainable adapter parameters against roughly 4.4B in MedGemma-4B). Adapters sit in attention (q, k, v, o) and MLP (gate, up, down) projections on layers 15-19.
Model
Targeted LoRA
Dataset
Not dataset-specific
Split
patient-disjoint
Comparison
Full LoRA modifies all 34 layers with 30.5M parameters (0.72%), about 7x more.
Uncertainty
not reported for this value
Thesis
Chapter 5, Chapter 5, targeted LoRA configuration
Source artifact
dissertation/chapters/04_thrust3.tex
Last verified
2026-07-15

76.8%

question-level

The adapter buys consistency by leaning harder on the question text

This is the reframe that changes the story. On the clean patient-disjoint audit, text-only agreement rises from 53.5% for the base model to 76.8% (+/- 3.1) after targeted adaptation: the adapted model returns its original answer with the image deleted 23 points more often. Both adapters get much of their new consistency by relying more on the question text, not by making steadier use of the image. Targeted and Full LoRA are statistically tied on image reliance (76.8% +/- 3.1 vs 76.6% +/- 1.7).

Targeted LoRA · MIMIC-CXR · n = 241 · +/- 3.1 (standard deviation over five seeds)

Source, denominator, and limits
How far this goesThe claim that targeted adaptation preserves image dependence relative to full adaptation is NOT supported on the clean adapters: the two are tied on both image-reliance measures, and both move the model toward the text. The older ordering (targeted 66.3% text-only / 32.7% swap vs full 80.6% / 14.3%) is specific to the original-pipeline adapters and does not replicate. Targeted LoRA remains the recommendation only because at tied consistency it keeps accuracy (84.3% vs 82.4%) at 0.1% of parameters instead of 0.72%: it is the cheaper and more accurate route to the same consistency, not a more grounded one. This audit is the reason a low flip rate cannot be read as evidence of visual reasoning. The displayed value is the mean over five seeds (per-seed: 78.8, 80.5, 74.3, 77.6, 73.0).
Metric
text-only agreement (share of predictions unchanged when the image is removed), presence-of-finding endpoint
Denominator
question-level (per question)
Sample
n = 241
Model
Targeted LoRA
Dataset
MIMIC-CXR
Split
patient-disjoint
Comparison
53.5% for the base model, a 23-point increase; Full LoRA reaches a statistically indistinguishable 76.6% +/- 1.7. Swap sensitivity also falls slightly (base 22.0%, targeted 19.9% +/- 1.2, full 20.9% +/- 1.3).
Uncertainty
+/- 3.1 (standard deviation over five seeds)
Thesis
Chapter 5, tab:pd_safety_audit
Source artifact
results/lora_fleet_patient_disjoint/audit_targeted_s{42,123,456,789,2024}.json (mean over five seeds)
Last verified
2026-07-15

73%

pairwise

Transplanting the residual at layer 16 flips the answer on 73% of pairs

Swapping a single layer's residual stream from a 'detecting' pair member into a 'missing' one flips the model's committed answer on 73% of pairs at layer 16, up from 35% at layer 15 and 8% at layer 14, reaching 100% by layer 20. The answer commits in a narrow band at layer 16 (median commit layer 16, 95% bootstrap CI [16, 16]). An image-token control never exceeds 0.0007, so this is not a generic patching artifact.

MedGemma-4B · PadChest · n = 1,396 · median per-pair commit layer 16, 95% bootstrap CI [16, 16]

Source, denominator, and limits
How far this goesThis is a GENERAL answer-commitment locus, not a paraphrase-specific mechanism: the same layers 16-17 band governs left/right localization (on a small 7-pair test), so it localizes where this model commits to any answer, not where paraphrase sensitivity uniquely lives. The pool is 94% positive (1,314 yes vs 82 no), so the result is established on predominantly positive items; 107 of 1,396 pairs (7.7%) carry an anatomical qualifier on one side only and are not strict paraphrases. The transplant was run in a separate codebase and the final pair-pool script was NOT retained, so the pool is reconstructible by inspection but not re-executable. Layer 16 (the causal commit) must not be conflated with layer 17 (where Feature 3818 sits), though the single-layer offset falls inside the same band.
Metric
answer flip rate under single-layer residual transplant at layer 16
Denominator
pairwise (per question-paraphrase pair)
Sample
n = 1,396
Model
MedGemma-4B
Dataset
PadChest
Split
eval
Comparison
8% at layer 14 and 35% at layer 15, rising to 100% by layer 20; 71% of pairs commit within layers 15-17.
Uncertainty
median per-pair commit layer 16, 95% bootstrap CI [16, 16]
Thesis
Chapter 5, fig:t3_commit_curve
Source artifact
dissertation/chapters/04_thrust3.tex
Last verified
2026-07-15

37 of 76 (48.7%)

pairwise

Feature 3818 is the largest layer-17 change in 37 of 76 same-polarity flips

Restricting to the 141 FlipBank pairs that preserve the question's operator (dropping 17 polarity switches where changing the answer is correct), the model flips on 76. On those, Feature 3818 is the single largest layer-17 activation change in 37 of them (48.7%) and in the top five in 59. It is a prominent contributor to same-polarity paraphrase flips.

MedGemma-4B · Multiple datasets · n = 76

Source, denominator, and limits
How far this goesBeing the largest delta is an association, not a cause. Single-feature ablation of 3818 restores the original answer in only 6 of the 76 pairs and recovers about 10% of the margin shift on average: it is a contributing, not sufficient, cause. Feature 3818 is primarily an operator/register feature encoding whether a question asks about presence or exclusion, a clinically real distinction rather than a bug, so its prominence partly reflects the job it is built for. The whole account is EXPLORATORY pending a same-polarity screen with matched non-flip controls. Feature 12139 topping all 76 is itself a caution rather than a result: any feature tracking the yes/no decision will top the ranking on any flip.
Metric
share of operator-preserving flipped pairs where Feature 3818 is the largest layer-17 activation delta
Denominator
pairwise (per question-paraphrase pair)
Sample
n = 76
Model
MedGemma-4B
Dataset
Multiple datasets
Split
eval
Comparison
Feature 3818 is in the top five layer-17 deltas in 59 of the 76; the downstream layer-29 decision feature 12139 is the largest delta in all 76.
Uncertainty
not reported for this value
Thesis
Chapter 5, Chapter 5, sec:feature_3818
Source artifact
dissertation/chapters/04_thrust3.tex
Last verified
2026-07-15

28%

case-level

Patching Feature 3818 recovers 28% of the answer margin, 3.5x controls

On a pleural effusion example, rephrasing 'Is there pleural effusion?' as 'Is pleural fluid present?' drives the yes-minus-no margin from +8.75 to -0.625, flipping the answer to No. Writing Feature 3818's original activation back in recovers the margin to +2.0 and restores the Yes, about 28% of the lost margin, against 8% for ten control features.

MedGemma-4B · Multiple datasets · n = 1

Source, denominator, and limits
How far this goesThis is one exemplar plus a 20-pair distributional sample, NOT a population estimate. The 25% top-contributor share is a property of a small, operator-mixed sample rather than an estimate of how many flips Feature 3818 explains in the wild; the remaining 65% of sampled pairs are driven by distributed mechanisms with no single dominant feature. The four other generalization pairs in the same artifact did not flip at all, so the patch's behaviour on flips rests on this single case. Treat the causal language as exploratory pending same-polarity controls with matched non-flip pairs.
Metric
share of yes-minus-no margin recovered by patching Feature 3818 at layer 17
Denominator
case-level (per case)
Sample
n = 1
Model
MedGemma-4B
Dataset
Multiple datasets
Split
eval
Comparison
8% average margin recovery for ten control features sampled from the top 100 by activation magnitude, making Feature 3818's effect 3.5x larger.
Uncertainty
not reported for this value
Thesis
Chapter 5, Chapter 5, sec:feature_3818
Source artifact
results/sae_analysis/feature_3818_full_analysis.json
Last verified
2026-07-15

0.26

question-level

The best layers to fix are not the layers where the mechanism lives

Adapting early layers (0-10) reduces the margin difference to 0.26, an 86% cut from the 1.87 baseline, while the mechanistically indicated layers 15-19 reach only 0.38 (80%). Even a randomly chosen block (layers 5-9) does better at 0.30. The SAE analysis says where paraphrase sensitivity manifests, but the best place to intervene is upstream, before the register-sensitive representation forms. RQ3.3 is answered no.

Targeted LoRA · MIMIC-CXR · n = 355

Source, denominator, and limits
How far this goesThe comparison is not layer-count-matched: early (0-10) spans 11 layers against 5 for middle (15-19), so part of the 6-point gap may buy capacity rather than location. The endpoint is margin difference on a validation split, not flip rate on the held-out test, and these five configurations come from the ORIGINAL training pipeline (500 pairs, 3 epochs), not the clean patient-disjoint fleet that produces the headline 59% reduction, so they are reported for relative patterns only. A separate 50-config block sweep supports the same U-shaped conclusion with its global minimum at layers 1-5. Note that the deployed adapter still targets 15-19: the ablation undercuts the mechanistic rationale for that choice without changing the headline result.
Metric
mean margin difference (yes-minus-no logit gap between original and paraphrase) after adapting layers 0-10
Denominator
question-level (per question)
Sample
n = 355
Model
Targeted LoRA
Dataset
MIMIC-CXR
Split
eval
Comparison
0.38 for the mechanistically indicated layers 15-19
Uncertainty
not reported for this value
Thesis
Chapter 5, tab:thrust3_layer_ablation
Source artifact
dissertation/tables/thrust3/table_layer_ablation.tex
Last verified
2026-07-15

91.6%

question-level

The MIMIC-trained adapter gains 6.4 accuracy points on Spanish data

An adapter trained only on US MIMIC-CXR data raises accuracy on balanced PadChest questions from 85.2% to 91.6%, and sharpens the margin difference from 1.02 to 0.25 (a 75% reduction). The benefit transfers across institution, equipment and patient population, but it shows up as accuracy and confidence, not as fewer flips.

Targeted LoRA · PadChest · n = 250

Source, denominator, and limits
How far this goesTransfer shows up as accuracy and margin sharpening, NOT as flip reduction: the base model was already consistent on this balanced set (7.6% flip), leaving almost no headroom, so the flip rate moves only to 6.8%. Reading this as evidence that the adapter fixes paraphrase sensitivity out of distribution would be wrong. These are ORIGINAL-pipeline adapters (500 pairs, 3 epochs), not the clean patient-disjoint fleet, so they are reported for relative patterns and are not interchangeable with the headline mitigation numbers. The 250-question balanced set is also deliberately unrepresentative of PadChest, which is 83% positive, so this accuracy is not comparable to any rate on the 861-question flip bank or the main benchmark.
Metric
accuracy on the balanced PadChest transfer set
Denominator
question-level (per question)
Sample
n = 250
Model
Targeted LoRA
Dataset
PadChest
Split
ood
Comparison
85.2% for the base model, a gain of 6.4 percentage points; margin difference falls from 1.02 to 0.25 (75.4% reduction) while flip rate barely moves (7.6% to 6.8%).
Uncertainty
not reported for this value
Thesis
Chapter 5, tab:thrust3_padchest_transfer
Source artifact
dissertation/tables/thrust3/table_padchest_transfer.tex
Last verified
2026-07-15

81% (range 45-100%)

model-dataset

81% of consistent predictions are image-invariant

Averaged across ten model-dataset settings, 81% of each model's consistent predictions are unchanged when the image is removed (range 45-100%), so a low paraphrase-flip rate is not evidence of grounded visual reasoning.

Multiple models · Multiple datasets · n = 10 · unweighted mean over 10 settings; range 45-100%

Source, denominator, and limits
How far this goesStated deliberately as a level, not a correlation: the r = -0.86 between flip rate and the image-invariant fraction is largely definitional (the fraction is by construction a subset of consistent predictions, and (1-flip) alone correlates r = +0.86) and falls to r = -0.15 once conditioned on consistency. The level, not the slope, is the finding.
Metric
mean share of consistent predictions that are image-invariant (text-only agreement)
Denominator
model-dataset (per model-dataset setting)
Sample
n = 10
Model
Multiple models
Dataset
Multiple datasets
Split
eval
Comparison
range 45-100% across the ten model-dataset settings
Uncertainty
unweighted mean over 10 settings; range 45-100%
Thesis
Chapter 6, tab:quadrant_counts
Source artifact
dissertation/tables/thrust4/table_quadrant_counts.tex
Last verified
2026-07-15

84.1%

question-level

Targeted LoRA on PadChest: 84.1% of questions in the Dangerous quadrant

After targeted LoRA adaptation, 84.1% of PadChest questions land in the Dangerous quadrant (consistent under paraphrase but not image-reliant): the adapter buys consistency largely by leaning on the text prior.

Targeted LoRA · PadChest · n = 861

Source, denominator, and limits
How far this goesThese are the ORIGINAL-PIPELINE adapters on the curated 861-question PadChest flip bank, not the clean patient-disjoint mitigation fleet. Correctness is a separate axis: on PadChest the Dangerous cell is often MORE accurate than the grounded (Ideal) cell because high finding base rates make the text prior usually right, so this behavioral screen is not a per-prediction safety verdict.
Metric
share of questions classified Dangerous (consistent + text-reliant)
Denominator
question-level (per question)
Sample
n = 861
Model
Targeted LoRA
Dataset
PadChest
Split
ood
Comparison
Base MedGemma-4B 22.5%, Full LoRA 60.6% on the same 861 PadChest questions
Uncertainty
not reported for this value
Thesis
Chapter 6, tab:quadrant_counts
Source artifact
dissertation/tables/thrust4/table_quadrant_counts.tex
Last verified
2026-07-15

22.5%

question-level

Base MedGemma-4B on PadChest: 22.5% Dangerous

Base MedGemma-4B places only 22.5% of PadChest questions in the Dangerous quadrant, but this is low only because the model is too inconsistent to land in any consistent cell at all: 74% of its predictions fall in Fragile or Worst.

MedGemma-4B · PadChest · n = 861

Source, denominator, and limits
How far this goesOriginal-pipeline audit (not the clean patient-disjoint fleet). A low Dangerous share here does not mean Base is safer: it reflects inconsistency, not grounding. And correctness is a separate axis: on PadChest the Dangerous cell is often MORE accurate than the grounded cell (Base 100% vs 33%), so the behavioral screen is not a per-prediction safety verdict.
Metric
share of questions classified Dangerous (consistent + text-reliant)
Denominator
question-level (per question)
Sample
n = 861
Model
MedGemma-4B
Dataset
PadChest
Split
ood
Comparison
Targeted LoRA 84.1%, Full LoRA 60.6% on the same 861 questions; Base's Ideal/Fragile/Worst shares are 3.5/19.5/54.5%
Uncertainty
not reported for this value
Thesis
Chapter 6, tab:quadrant_counts
Source artifact
dissertation/tables/thrust4/table_quadrant_counts.tex
Last verified
2026-07-15

78.6%

question-level

Full LoRA on MIMIC: 78.6% Dangerous

Full LoRA, the variant with the lowest flip rate in-distribution, places 78.6% of MIMIC questions in the Dangerous quadrant: consistent answers that do not change when the image is removed.

Full LoRA · MIMIC-CXR · n = 98

Source, denominator, and limits
How far this goesOriginal-pipeline adapters on the 98-question MIMIC flip bank, not the clean patient-disjoint fleet. Correctness is a separate axis (the Dangerous cell is often MORE accurate than the grounded cell), so a behavioral screen classifies risk exposure, not per-prediction safety.
Metric
share of questions classified Dangerous (consistent + text-reliant)
Denominator
question-level (per question)
Sample
n = 98
Model
Full LoRA
Dataset
MIMIC-CXR
Split
in-distribution
Comparison
Base MedGemma-4B 25.5%, Targeted LoRA 59.2% on the same 98 MIMIC questions
Uncertainty
not reported for this value
Thesis
Chapter 6, tab:quadrant_counts
Source artifact
dissertation/tables/thrust4/table_quadrant_counts.tex
Last verified
2026-07-15

r = -0.15 (conditioned)

model-dataset

The flip-rate vs Dangerous-fraction correlation is largely definitional

The headline r = -0.86 correlation between flip rate and the Consistent-Text-driven fraction is largely a definitional consequence of that fraction being a subset of consistent predictions; conditioning on consistency reduces it to r = -0.15.

Multiple models · Multiple datasets · n = 10 · Spearman -0.13 conditioned; 10 data points

Source, denominator, and limits
How far this goesReport the level (81% of consistent predictions image-invariant, range 45-100%), not the slope. The unconditioned r = -0.86 (and the older public r = -0.89, now retracted) should never be presented as an empirical finding: the (1-flip) factor alone correlates r = +0.86 by construction.
Metric
Pearson r between flip rate and image-invariant share of consistent predictions, conditioned on consistency
Denominator
model-dataset (per model-dataset setting)
Sample
n = 10
Model
Multiple models
Dataset
Multiple datasets
Split
eval
Comparison
-0.86 unconditioned
Uncertainty
Spearman -0.13 conditioned; 10 data points
Thesis
Chapter 6, tab:quadrant_counts
Source artifact
dissertation/chapters/05_thrust4.tex
Last verified
2026-07-15

rho = -0.09 (Base)

case-level

Attention rank does not predict causal patch importance (rho = -0.09)

Patch-rank Spearman correlation between attention weight and occlusion importance is -0.09 for Base MedGemma-4B: attention magnitude does not indicate which image patches the model causally uses.

MedGemma-4B · PadChest · n = 637

Source, denominator, and limits
How far this goesRho is near zero across all three MedGemma variants (-0.09 / -0.04 / +0.06), so the negative result is not model-specific, and no faithful attention-based alternative was found. This does not mean the region is causally unused: ROI causal scores exclude zero for all three models. Attention is coarsely localized but not faithful.
Metric
patch-rank Spearman rho, attention weight vs occlusion importance
Denominator
case-level (per case)
Sample
n = 637
Model
MedGemma-4B
Dataset
PadChest
Split
ood
Comparison
Targeted LoRA -0.04, Full LoRA +0.06 on the same 637 boxes
Uncertainty
not reported for this value
Thesis
Chapter 6, tab:grounding
Paper
Attention Without Grounding: Causal Evaluation of Visual Explanations in Medical VLMs
Source artifact
dissertation/tables/thrust4/table_grounding.tex
Last verified
2026-07-15

0.296 true box vs 0.261 shifted

case-level

Attention coverage barely beats a shifted box (0.296 vs 0.261)

Base MedGemma-4B places 29.6% of attention mass inside the radiologist-annotated pathology box, only marginally more than the 26.1% it places in a same-sized shifted box: attention is a coarse localizer, not a faithful one.

MedGemma-4B · PadChest · n = 637

Source, denominator, and limits
How far this goesThe grounding-637 subset is all-positive PadChest cases with radiologist boxes and must not be pooled with other evaluation sets. Coverage far exceeds the random-far baseline, so attention is not random; the failure is precision, not localization altogether. The pattern holds for Targeted (0.353 vs 0.310) and Full LoRA (0.385 vs 0.338).
Metric
true-box attention coverage vs shifted-box baseline
Denominator
case-level (per case)
Sample
n = 637
Model
MedGemma-4B
Dataset
PadChest
Split
ood
Comparison
shifted-box 0.261; random-far 0.056 (roughly 5-6x above random, marginally above shifted)
Uncertainty
not reported for this value
Thesis
Chapter 6, tab:grounding
Paper
Attention Without Grounding: Causal Evaluation of Visual Explanations in Medical VLMs
Source artifact
dissertation/tables/thrust4/table_grounding.tex
Last verified
2026-07-15

0.012

question-level

Targeted LoRA has the smallest between-sex ECE gap (0.012)

Targeted LoRA shows the smallest between-sex calibration gap of the three MedGemma variants (ECE 0.106 male vs 0.118 female, gap 0.012) on PadChest.

Targeted LoRA · PadChest · n = 861

Source, denominator, and limits
How far this goesSmallest sex ECE gap, but the same model has the steepest age accuracy gradient (13 pp) and a 2.7 pp sex accuracy gap slightly wider than Base's 2.0 pp: no model is uniformly most equitable, the sex and age axes disagree. PadChest only; MIMIC has no demographics on disk, so this analysis cannot be replicated in-distribution.
Metric
absolute between-sex expected-calibration-error difference
Denominator
question-level (per question)
Sample
n = 861
Model
Targeted LoRA
Dataset
PadChest
Split
ood
Comparison
Base 0.016, Full LoRA 0.041 (largest disparities on every axis, ECE from 0.249 to 0.332 across every group)
Uncertainty
not reported for this value
Thesis
Chapter 6, tab:fairness
Paper
Predictive Entropy as a Joint Screen for Error and Paraphrase Instability in Medical Vision-Language Models
Source artifact
results/uai/fairness_analysis.json
Last verified
2026-07-15

13 pp (83.3% under-40 to 96.3% over-80)

question-level

Targeted LoRA accuracy climbs 13 pp from youngest to oldest patients

Targeted LoRA accuracy on PadChest rises from 83.3% for patients under 40 to 96.3% for patients over 80, a 13 percentage-point age gradient with younger patients least accurate - the steepest of the three MedGemma variants.

Targeted LoRA · PadChest · n = 861

Source, denominator, and limits
How far this goesThe youngest bin is small (n = 48), so its 83.3% estimate is noisy. The gradient may also reflect age-correlated case mix (finding prevalence differs by age) rather than a pure model disparity. PadChest only; no demographic data is available for the MIMIC set.
Metric
accuracy range across age bins (<40, 40-60, 60-80, 80+)
Denominator
question-level (per question)
Sample
n = 861
Model
Targeted LoRA
Dataset
PadChest
Split
ood
Comparison
Base is nearly flat across age (76.8-81.1%); Full LoRA spans 63.1-71.3% at much lower overall accuracy
Uncertainty
not reported for this value
Thesis
Chapter 6, tab:fairness
Paper
Predictive Entropy as a Joint Screen for Error and Paraphrase Instability in Medical Vision-Language Models
Source artifact
results/uai/fairness_analysis.json
Last verified
2026-07-15

0.823

question-level

Single-pass entropy predicts paraphrase flips (AUROC 0.823)

Softmax predictive entropy from one forward pass predicts whether a prediction will flip under paraphrasing with AUROC 0.823 for Targeted LoRA on PadChest: uncertainty is a deployable single-pass proxy for paraphrase fragility.

Targeted LoRA · PadChest · n = 861 · 95% CI [0.778, 0.864]; p = 4.3e-29

Source, denominator, and limits
How far this goesSoftmax entropy, temperature-scaled entropy and absolute margin are rank-equivalent for a binary softmax, so they are one signal reported three ways, not three independent methods; only the deep ensemble is genuinely distinct, and it fails (0.552). Operating thresholds are model-specific and do not transfer.
Metric
flip-prediction AUROC from softmax predictive entropy
Denominator
question-level (per question)
Sample
n = 861
Model
Targeted LoRA
Dataset
PadChest
Split
ood
Comparison
operator-preserving slice 0.826 (95% CI [0.780, 0.870]); deep ensemble 0.552; cross-architecture LLaVA-Rad LoRA 0.830 (PadChest) / 0.905 (MIMIC)
Uncertainty
95% CI [0.778, 0.864]; p = 4.3e-29
Thesis
Chapter 7, tab:bridge_auroc
Paper
Predictive Entropy as a Joint Screen for Error and Paraphrase Instability in Medical Vision-Language Models
Source artifact
dissertation/tables/thrust4/table_bridge_auroc.tex
Last verified
2026-07-15

0.862

question-level

Single-pass entropy detects errors (AUROC 0.862)

The same single-pass softmax entropy signal detects wrong answers with AUROC 0.862 for Targeted LoRA on PadChest, matching Monte Carlo dropout (0.856) and beating the deep ensemble (0.751).

Targeted LoRA · PadChest · n = 861

Source, denominator, and limits
How far this goesSoftmax entropy, temperature-scaled entropy and absolute margin are rank-equivalent, so this is the same underlying signal as the flip-prediction AUROC, not independent corroboration; thresholds are model-specific. High AUROC ranks confidence but does not certify that the confident predictions are image-grounded.
Metric
error-detection AUROC from softmax predictive entropy
Denominator
question-level (per question)
Sample
n = 861
Model
Targeted LoRA
Dataset
PadChest
Split
ood
Comparison
Monte Carlo dropout 0.856; deep ensemble 0.751; flip-prediction AUROC 0.823 on the same set
Uncertainty
not reported for this value
Thesis
Chapter 7
Paper
Predictive Entropy as a Joint Screen for Error and Paraphrase Instability in Medical Vision-Language Models
Source artifact
dissertation/chapters/06_thrust5.tex
Last verified
2026-07-15

0.016

question-level

Targeted LoRA has the best selective-prediction quality OOD (AUGRC 0.016)

With temperature scaling, Targeted LoRA reaches AUGRC 0.016 on PadChest, the best generalized risk-coverage score of the MedGemma variants and roughly a third of Base's 0.047.

Targeted LoRA · PadChest · n = 732

Source, denominator, and limits
How far this goesTemperature scaling withholds a 15% calibration holdout, so 732 of the 861 PadChest questions are evaluated. AUGRC measures confidence ranking, not grounding: the same model puts 84.1% of these questions in the Dangerous (consistent but text-reliant) quadrant, so good selective prediction can coexist with image-invariant answers.
Metric
area under the generalized risk-coverage curve (temperature-scaled)
Denominator
question-level (per question)
Sample
n = 732
Model
Targeted LoRA
Dataset
PadChest
Split
ood
Comparison
Base MedGemma-4B 0.047, Full LoRA 0.156 (same method, same set)
Uncertainty
not reported for this value
Thesis
Chapter 7, tab:clean_calibration
Paper
Predictive Entropy as a Joint Screen for Error and Paraphrase Instability in Medical Vision-Language Models
Source artifact
dissertation/tables/thrust4/table_clean_calibration.tex
Last verified
2026-07-15

0.156

question-level

Full LoRA collapses OOD (AUGRC 0.156)

Full LoRA, despite the lowest in-distribution flip rate, has the worst out-of-distribution selective prediction: AUGRC 0.156 on PadChest, more than three times Base's 0.047, alongside ECE 0.229.

Full LoRA · PadChest · n = 732

Source, denominator, and limits
How far this goesTemperature scaling withholds a 15% calibration holdout (732 of 861 questions evaluated). The failure is consistent with text memorization: the all-layer adapter fits MIMIC text patterns, so its confidence transfers poorly to PadChest. This is an original-pipeline adapter, not the clean patient-disjoint fleet.
Metric
area under the generalized risk-coverage curve (temperature-scaled)
Denominator
question-level (per question)
Sample
n = 732
Model
Full LoRA
Dataset
PadChest
Split
ood
Comparison
Base MedGemma-4B 0.047, Targeted LoRA 0.016 (same method, same set)
Uncertainty
not reported for this value
Thesis
Chapter 7, tab:clean_calibration
Paper
Predictive Entropy as a Joint Screen for Error and Paraphrase Instability in Medical Vision-Language Models
Source artifact
dissertation/tables/thrust4/table_clean_calibration.tex
Last verified
2026-07-15

33.0% admitted at 96.8% accuracy

question-level

The readiness audit admits 33.0% of Targeted LoRA PadChest predictions at 96.8% accuracy

The deployment-readiness rule (paraphrase agreement AND image reliance) admits 33.0% of Targeted LoRA's PadChest predictions, and the admitted slice is 96.8% accurate versus 91.5% unfiltered, with a 4.2% residual flip rate on uninspected paraphrases.

Targeted LoRA · PadChest · n = 861

Source, denominator, and limits
How far this goesThis is an OFFLINE readiness audit, not an online gate: the roi_matters term requires a radiologist bounding box for the finding, which a deployed system does not have for an undiagnosed patient (boxes exist for 637 of 861 PadChest records and none of MIMIC). Dropping the region term - the only online-deployable variant - cuts admission from 33.0% to 26.9%.
Metric
admission rate of the offline readiness audit
Denominator
question-level (per question)
Sample
n = 861
Model
Targeted LoRA
Dataset
PadChest
Split
ood
Comparison
accuracy 96.8% gated vs 91.5% on all; Base admits 41.6% (96.9% gated), Full LoRA 26.7% (83.9% gated)
Uncertainty
not reported for this value
Thesis
Chapter 7, tab:gate_results
Paper
Consistency Is Not Safety: Deployment Audits Reveal Text-Driven Selection in Medical Vision-Language Models
Source artifact
dissertation/tables/thrust4/table_gate_results.tex
Last verified
2026-07-15

2.9%

question-level

2.9% accuracy on the slice where the image should matter

Targeted LoRA scores only 2.9% accuracy on the PadChest text-disagrees slice - the cases where the image-conditioned answer must depart from the text-only answer - so gates raise admitted-case accuracy by selecting text-answerable cases rather than image-grounded ones (RQ5.3 = no).

Targeted LoRA · PadChest · n = 241

Source, denominator, and limits
How far this goesA negative result, and it should be read as one: apparent safety gains from gating come from routing around the image-relevant portion of the workload, not from solving it. Severity is dataset-dependent (the same slice on MIMIC-CXR sits at 68-86% accuracy), so the lesson is that high-answerability environments amplify the text shortcut, not that all medical VLMs always fail when the image matters.
Metric
accuracy on the text-disagrees slice
Denominator
question-level (per question)
Sample
n = 241
Model
Targeted LoRA
Dataset
PadChest
Split
ood
Comparison
the strict gating rule raises this only to 25.0% at 2% intra-slice coverage; Qwen2-VL gets 0 of 279 text-disagrees questions correct under every evaluated rule
Uncertainty
not reported for this value
Thesis
Chapter 7
Paper
Consistency Is Not Safety: Deployment Audits Reveal Text-Driven Selection in Medical Vision-Language Models
Source artifact
dissertation/chapters/06_thrust5.tex
Last verified
2026-07-15

0.0001

question-level

Adapter-only Monte Carlo dropout detects almost no epistemic uncertainty (MI = 0.0001 nats)

Monte Carlo dropout on the LoRA adapters yields mean mutual information of 0.0001 nats for Targeted LoRA on PadChest: the perturbation barely moves the model, so the probe detects almost none of the predictive entropy as epistemic.

Targeted LoRA · PadChest · n = 861

Source, denominator, and limits
How far this goesNear-zero mutual information does NOT establish that the remaining uncertainty is aleatoric: a probe that perturbs 0.1% of the weights (4.38M adapter parameters of roughly 4.4B) cannot see epistemic uncertainty carried by the frozen 99.9%. The defensible conclusion is that adapter-only dropout is an uninformative epistemic probe for this model, which is also why it adds nothing over a single softmax pass.
Metric
mean mutual information (epistemic component) under Monte Carlo dropout, p = 0.05 on adapters
Denominator
question-level (per question)
Sample
n = 861
Model
Targeted LoRA
Dataset
PadChest
Split
ood
Comparison
deep ensemble mean MI 0.057 nats, but that signal is corrupted by member OOD failure; Monte Carlo dropout flip AUROC equals plain softmax entropy (0.823 vs 0.823)
Uncertainty
not reported for this value
Thesis
Chapter 7
Paper
Predictive Entropy as a Joint Screen for Error and Paraphrase Instability in Medical Vision-Language Models
Source artifact
dissertation/chapters/06_thrust5.tex
Last verified
2026-07-15

52.0% worst member vs 92.1% best

question-level

Deep ensemble fails out-of-distribution: worst member at 52.0% accuracy

The five-seed LoRA deep ensemble fails on PadChest: per-member accuracy spans 52.0% (seed 456) to 92.1% (seed 42), and the ensemble's selective prediction collapses to AURC 0.130 versus 0.019 for single-pass entropy, admitting only 23.8% of cases at 5% risk versus 88.5%.

Targeted LoRA · PadChest · n = 861 · member accuracies 52.0 / 67.1 / 68.5 / 78.7 / 92.1% across the 5 seeds

Source, denominator, and limits
How far this goesTrain-validation accuracy of 86.9-88.9% gave no early warning of which seeds would fail, so the failure is undetectable before deployment shift. The ensemble works in-distribution on MIMIC (best calibration and accuracy there), and the LLaVA-Rad ensemble does not collapse: the failure is specific to adapters trained on the same narrow 500-pair MIMIC distribution, not to ensembling in general.
Metric
worst ensemble-member accuracy on PadChest
Denominator
question-level (per question)
Sample
n = 861
Model
Targeted LoRA
Dataset
PadChest
Split
ood
Comparison
best member (seed 42) 92.1%; ensemble AURC 0.130 vs 0.019 single-pass entropy; probability averaging recovers 72.6% aggregate accuracy but not a usable confidence ranking
Uncertainty
member accuracies 52.0 / 67.1 / 68.5 / 78.7 / 92.1% across the 5 seeds
Thesis
Chapter 7
Paper
Predictive Entropy as a Joint Screen for Error and Paraphrase Instability in Medical Vision-Language Models
Source artifact
results/uai/revision/ensemble_diagnostics.json
Last verified
2026-07-15

88.7%

question-level

Conformal coverage slips to 88.7% at severity-5 corruption (90% target)

Base MedGemma-4B's split-conformal prediction sets hold 91.8% empirical coverage clean against a 90% target but slip to 88.7% under severity-5 image corruption.

MedGemma-4B · PadChest · n = 732

Source, denominator, and limits
How far this goesDescriptive coverage only: corruption breaks the exchangeability assumption between calibration and test data, so no formal validity guarantee applies under shift. Full LoRA's near-constant coverage is achieved degenerately - image corruption barely affects text-driven predictions - so stable coverage alone does not imply clinical utility.
Metric
empirical conformal coverage at 90% nominal target under severity-5 corruption
Denominator
question-level (per question)
Sample
n = 732
Model
MedGemma-4B
Dataset
PadChest
Split
ood
Comparison
91.8% clean, 91.2% at severity 3; Targeted LoRA 93.7 -> 92.2 -> 89.9; Full LoRA 88.7 -> 88.8 -> 87.2
Uncertainty
not reported for this value
Thesis
Chapter 7, tab:conformal_shift
Paper
Predictive Entropy as a Joint Screen for Error and Paraphrase Instability in Medical Vision-Language Models
Source artifact
results/uai/week2_analysis/conformal_shift.json
Last verified
2026-07-15

Population reference

These evaluation sets are not interchangeable. This table exists so a number on this site can never be quietly compared against a number it does not belong with.

Evaluation populations in the dissertation
PopulationSizeWhat it isWhy it does not pool
PSF-Med main benchmark 92,856 pairs The full evaluated benchmark: 92,856 final evaluation pairs built from 26,850 original clinical questions (MIMIC-CXR 8,938 pairs, PadChest v2 59,573, VinDr-CXR 24,345; mean 3.5 paraphrases per question). Source of the headline flip rates. The 92,856 evaluation pairs are numerically close to the ~92,000 construction-stage candidate paraphrases by coincidence and are NOT a subset of the 61,761 audit-retained core (PadChest v1 audited pairs were superseded by regenerated v2). Never equate pipeline stages, and never compare these rates with curated flip-bank rates.
Binary yes/no subset 49,932 pairs The binary presence-question subset of the main benchmark used for headline pairwise flip rates: MIMIC-CXR 1,539 questions / 5,076 pairs; PadChest 8,445 / 36,244; VinDr-CXR 2,807 / 8,612. Pairwise flip rates on this subset are roughly half the query-level rates on the same questions (MedGemma-4B PadChest: 13.4% pairwise vs 32.2% query-level). Never mix the two denominators, and never compare these rates against curated flip-bank rates.
Mechanistic FlipBank (158 pairs) 158 pairs A 158-pair curated set of confirmed flips (94 yes-to-no, 64 no-to-yes, 142 images, 23 findings) used for the sparse-autoencoder mechanistic analyses on MedGemma-4B. Every pair in this set flips by construction, so any flip statistic computed on it is 100% by design; it exists to study mechanisms, never to estimate flip prevalence or to compare against benchmark rates.
Three-backend diagnostic set (107 pairs) 107 pairs A curated 107-pair set of semantically close question-paraphrase pairs (BioClinicalBERT cosine similarity > 0.95) on which MedGemma-4B, MedGemma-27B, and LLaVA-Rad were all evaluated for the text-only, image-swap, and attention diagnostics. Constructed before the equivalence audit. MedGemma-4B flips 42.1% on this curated near-paraphrase set versus 8.3% on the benchmark; the two rates measure different things and must never be compared. The set also predates the equivalence audit, so its flip labels contain operator changes.
PadChest flip bank (861 questions) 861 questions An 861-question PadChest bank curated to be flip-prone, used for the four-quadrant audit, uncertainty quantification, the entropy bridge, and the deployment gate on MedGemma variants. This bank was curated to be flip-prone: Base MedGemma-4B flips 73.9% of it versus 13.4% pairwise on the PadChest benchmark. Quadrant shares, AUROCs, and gate numbers computed here must never be quoted as benchmark statistics.
MIMIC uncertainty set (98 questions) 98 questions The full 98-question MIMIC-CXR presence-question test set used for the four-quadrant audit and uncertainty analyses of the MedGemma variants; not truncated. Only 98 questions with no demographic attributes on disk: quadrant and calibration estimates carry wide intervals and cannot be pooled with the 861-question PadChest bank or the main benchmark.
LLaVA-Rad PadChest set (732 questions) 732 questions The 732-question subset of the PadChest flip bank on which LLaVA-Rad variants are evaluated (the temperature-scaling calibration holdout also leaves 732 of 861 for evaluation). A 732-question subset of the 861-question PadChest bank: LLaVA-Rad numbers computed here are not directly comparable to MedGemma numbers computed on the full 861 questions.
Patient-disjoint mitigation endpoint (238 questions) 238 questions The clean mitigation evaluation set: 238 presence-of-finding questions / 991 pairs over 236 held-out subjects and 238 held-out images, with zero subject and zero image overlap with LoRA training data. This is the only clean mitigation endpoint: its ~59% pairwise flip reduction (8.5% to 3.5%) supersedes the retracted 79.5% (a margin-difference reduction) and 69.6% (an older flip reduction) figures, and must not be compared with numbers from the original non-disjoint pipeline.
Attention analysis subsample (200 pairs) 200 pairs A 200-pair attention-analysis subsample balanced at 100 flip / 100 no-flip pairs, drawn from a 49,522-pair annotated pool. Balanced by construction at 50% flips: any aggregate flip statistic on this set is a design choice, not an observation, and attention contrasts here cannot be projected onto benchmark populations.
PadChest grounding subset (637 boxes) 637 boxes The 637 all-positive PadChest cases with radiologist bounding boxes used for attention-grounding coverage (true vs shifted vs random-far box) and ROI causal scoring. All 637 cases are positive findings with radiologist boxes: coverage and ROI scores say nothing about negative findings, and boxes exist for only 637 of the 861 PadChest bank questions and none of MIMIC, which is also why the gate's region term is not deployable online.
Clinician adjudication sample (1,200 pairs) 1,200 pairs A 1,200-pair stratified sample adjudicated by three clinician reviewers (400 pairs triple-reviewed; inter-reviewer agreement 98.5%, kappa 0.97) to validate the LLM equivalence judge (72.3% raw agreement, kappa 0.52). Drawn from the pre-regeneration v1 pool: only 95 of 174 MIMIC, 45 of 658 PadChest, and 0 of 43 VinDr pairs remain in the evaluated benchmark. It validates the equivalence judge, not the benchmark's flip rates; the earlier 0.9 pp sensitivity claim is retracted.
Canonical SAE screen (41,822 pairs) 41,822 pairs The canonical sparse-autoencoder screening pool: 4,542 MIMIC pairs (436 flips, 9.6%) and 37,280 PadChest pairs (2,833 flips, 7.6%) used for the two-stage L17/#3818 to L29/#12139 validation, mediation, and composition analyses. 171 of the 436 verified MIMIC flips (39%) are negation-pattern or operator changes, so restoration and mediation rates conflate correct operator handling with genuine same-polarity paraphrase sensitivity; do not read them as benchmark-level causal effect sizes.
Answer-commitment transplant pool (1,396 pairs) 1,396 pairs The 1,396-pair / 91-finding pool used for the residual-transplant study that locates the answer-commitment locus at layer 16 (flip rate 8% at L14, 73% at L16, 100% by L20; median commit layer 16, 95% bootstrap CI [16, 16]). The pool is 94% positive (1,314 yes vs 82 no), 7.7% of pairs carry one-sided qualifiers, and the pair-pool construction script was not retained; commit-layer statistics are specific to this pool and are evidence of a general commitment locus, not a paraphrase-specific mechanism.
Layer-ablation validation split (355 questions) 355 questions The 355-question MIMIC-CXR validation split used to compare LoRA layer ranges (early 0-10, random 5-9, all 0-33, middle 15-19, late 25-33) on margin difference. A validation split used for model selection, not the held-out test set or the patient-disjoint endpoint: margin-difference results here (early layers 0.26 beating the mechanistic middle 0.38) must not be quoted alongside patient-disjoint endpoint numbers.
PadChest transfer set (250 questions) 250 questions A 250-question class-balanced PadChest set (125 yes / 125 no) used as the out-of-distribution transfer test for the targeted LoRA (accuracy 85.2% to 91.6%, margin difference 1.02 to 0.25). Balanced 50/50 by construction, so the base model's 7.6% flip rate here sits far below its 13.4% pairwise PadChest benchmark rate; the transfer gain shows up in accuracy and margin sharpening, not in flip reduction, and neither number generalizes to the unbalanced benchmark.
Four-quadrant audit settings (10 model-dataset settings) Ten model-dataset settings; per-setting question counts differ (MIMIC 98 or 88, PadChest 861 or 732), so there is no single pooled n. The ten model-dataset settings of the four-quadrant safety screen, pooling the MIMIC flip banks (n=98 for MedGemma variants, n=88 for LLaVA-Rad variants) and the PadChest flip banks (n=861 and n=732) across five models. The 81% image-invariant share is a level averaged over ten settings whose underlying flip banks were curated to be flip-prone; it is not a benchmark statistic, and the r=-0.86 consistency-grounding correlation over the same ten points is largely definitional (conditioned on consistency it falls to r=-0.15).

Models and datasets

Models
ModelFamilyRole
MedGemma-4BMedGemma (Gemma 3)Google's 4B-parameter medical Gemma 3 instruction-tuned VLM (34 transformer layers, 256 image tokens); the dissertation's primary mechanistic subject and the base for both LoRA adapters.
MedGemma-1.5-4BMedGemma (Gemma 3)The 4B-parameter MedGemma 1.5 release, evaluated as one of the six base models in the PSF-Med benchmark (its VinDr cell is a degenerate yes-bias cell and is set aside).
MedGemma-27BMedGemma (Gemma 3)The 27B-parameter MedGemma model; the largest model evaluated, showing that scale does not buy paraphrase consistency (6.4-13.9% pairwise flips) and serving as one of the three diagnostic backends.
CheXoneQwen2.5-VLA 3B-parameter chest X-ray VLM built on Qwen2.5-VL, evaluated as one of the six base models in the PSF-Med benchmark.
LLaVA-RadLLaVAA 7B-parameter LLaVA-based radiology VLM whose low flip rates coincide with near-total text reliance, making it the central example of consistency without image use.
RadFMRadFM (custom)A 14B-parameter custom-architecture radiology foundation model; the least consistent model in the benchmark (up to 54.7% pairwise flips on VinDr-CXR).
Targeted LoRAMedGemma (Gemma 3)A mechanistically-placed LoRA adapter on MedGemma-4B layers 15-19 (rank 16, alpha 32, 4.38M parameters = 0.10%) that cuts pairwise flips about 59% without observed accuracy reduction, but raises text reliance rather than restoring grounding.
Full LoRAMedGemma (Gemma 3)A LoRA adapter across all 34 MedGemma-4B layers (30.5M parameters = 0.72%); slightly fewer flips than Targeted LoRA but statistically tied on image reliance, with worse accuracy, calibration, and demographic disparity.
LLaVA-Rad LoRALLaVAA LoRA-adapted LLaVA-Rad used as the cross-architecture check that the consistency-versus-text-reliance pattern and the entropy bridge (flip AUROC up to 0.905) are not MedGemma-specific.
Qwen2-VLQwen2-VLA general-domain Qwen2-VL model used in the grounded-slice analysis of the deployment gate, where it answers 0 of 279 text-disagrees cases correctly under every gating rule.
Datasets
DatasetAccessLicence
MIMIC-CXRCredentialed PhysioNet access (CITI training plus a signed data use agreement); redistribution of images or reports is prohibited, so the benchmark releases only question/paraphrase text and identifiers.PhysioNet Credentialed Health Data License 1.5.0
PadChestRegistration with the Medical Imaging Databank of the Valencia Region (BIMCV) is required before download. The PadChest Dataset Research Use Agreement grants research use only and prohibits reproducing or publishing any portion of the dataset without prior written permission, so the benchmark releases only question and paraphrase text.PadChest Dataset Research Use Agreement (BIMCV; research use only, redistribution prohibited without written permission)
VinDr-CXRCredentialed PhysioNet access (CITI training plus a signed data use agreement); redistribution prohibited.PhysioNet Credentialed Health Data License 1.5.0

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