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REVIEW 3 major objections 5 minor 151 references

The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making

T0 review · 3 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read Same clinical content, rephrased, moves LLM and human triage decisions in opposite directions.

desk verdict Valuable dataset, but the headline human-LLM sensitivity claims are undermined by paired statistics applied to unpaired human annotations. read the letter →

arxiv 2506.17163 v1 pith:UNCXMYP2 submitted 2025-06-20 cs.AI

Abinitha Gourabathina , Yuexing Hao , Walter Gerych , Marzyeh Ghassemi This is my paper · ORCID
classification cs.AI
keywords MedPerturbclinicalrobustnessLLMevaluationperturbationsensitivitygenderbiasstylevariationformathumanversusdecision-making
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper introduces MedPerturb, a dataset of 800 clinical vignettes in which the same medical content is re-expressed along three non-clinical axes: gender markers, language style (uncertain or colorful), and format (multiturn conversation or LLM summary). Using three triage questions—self-manage at home, seek a visit, and use extra resources—the authors compare four LLMs to 36 medical students across 7,200 human reads and 28,800 LLM reads. They find that LLMs change treatment recommendations more than clinicians when gender or tone changes, while clinicians change recommendations more than LLMs when the same case is presented as an AI summary or agentic conversation. The point of the paper is that surface-level rephrasing is not neutral: it can move model and human treatment decisions, and it moves them in opposite directions.

What carries the argument

The load-bearing object is the MedPerturb dataset and its paired baseline-perturbation structure: 200 base vignettes expanded to 800 contexts by three controlled transformations, each read by three clinicians and four LLMs on three binary triage questions, yielding 7,200 human and 28,800 model decisions. The argument runs on three metrics computed pairwise: Average Treatment Ratio (the fraction of yes answers per question), mutual information between baseline and perturbed decisions (stability), and percent change (flip rate). The dataset carries the claim because every comparison is within the same clinical content, so any measured shift is attributed to the perturbation rather than to new medical information.

What would settle it

Have each clinician read the original vignette and the AI summary of the same case in randomized order, then compute within-clinician flip rates for self-management and resource allocation. If the flip rate drops toward the rate seen for repeated reads of unchanged vignettes, the reported human sensitivity to format is an artifact of using different annotators for the two conditions.

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Extended reading notes

Core claim

The central discovery is that identical clinical content, rewritten along non-clinical axes, changes treatment decisions in opposite directions for machines and people. Across gender-swapped, gender-removed, uncertain-style, and colorful-style versions of cancer and patient-forum vignettes, the four LLMs altered their yes/no recommendations on self-management, visits, and resource allocation substantially more than clinicians did; clinician decisions had higher mutual information with the baseline, meaning they stayed more stable. The pattern reverses for format: when a vignette is turned into an AI-fabricated doctor-patient dialogue or a third-person LLM summary, clinicians shifted—recommending roughly 20–30% more self-management and fewer resource allocations—while LLM aggregate recommendations barely moved. The paper interprets this as evidence that static benchmarks miss the real failure mode: not whether the model knows the content, but whether surface cues hijack either the model's or the clinician's judgment.

Load-bearing premise

The claim that clinicians are more sensitive to format perturbations assumes baseline and perturbed clinician reads are paired, but the study assigned different annotators to each condition; if those annotator pools differ, the paired tests can attribute annotator differences to the perturbation instead of to the text format.

Editorial extensions

If this is right

  • Static accuracy benchmarks can hide clinically meaningful brittleness: two systems with identical aggregate treatment rates may still differ sharply in how much their decisions move under surface changes.
  • LLM-based triage tools may systematically under-recommend self-management and over-order labs and referrals, which could strain health systems if deployed without human oversight.
  • Gender and style cues that human clinicians ignore can change LLM recommendations, making fairness auditing of clinical LLMs a requirement rather than an afterthought.
  • Because AI summaries and agentic conversations move human clinicians toward more self-management and fewer resources, evaluating clinical summarizers by faithfulness scores alone will not capture their downstream effect on care decisions.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The opposite sensitivity directions imply that LLM-as-judge evaluations of clinical text could systematically misestimate human impact: models are insensitive to the exact format changes that move human clinicians.
  • A within-subject replication, in which each clinician reads both the original vignette and its AI summary, would test whether the reported human format-sensitivity is a genuine content effect or partly an artifact of comparing different annotator pools.
  • The correlation between conversation turn count and clinician self-management recommendations suggests that redundancy may signal lower acuity to humans; controlled rewrites that preserve information while varying redundancy could separate that signal from actual information loss.
  • Deployment studies for clinical LLMs should measure clinician behavior changes, not just model self-ratings, because the present results indicate that model and human sensitivities are negatively correlated.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. This paper introduces MedPerturb, a dataset of 800 clinical vignettes perturbed along three axes—gender, style, and format—with 7,200 human clinician reads and 28,800 LLM reads across three binary triage questions (MANAGE, VISIT, RESOURCE). Two case studies compare human and LLM sensitivity to these perturbations using average treatment rates (ATR), mutual information (MI), and percent change of recommendations (PC). The paper claims that LLMs are more sensitive to gender and style perturbations while human annotators are more sensitive to LLM-generated format perturbations such as clinical summaries, and that LLMs underrecommend self-management and overallocate resources relative to clinicians. The dataset and code are publicly released with detailed documentation of prompts, sampling procedures, annotator demographics, and IRB/consent information.

Significance. The public release of a systematically perturbed clinical vignette dataset with paired LLM and human clinician judgments is a potentially valuable resource for studying non-content robustness in clinical LLMs and human-AI alignment. The paper documents its data creation pipeline, model sampling, and annotator demographics in unusual detail, and the reproducibility materials (code, Hugging Face dataset, website) are concrete strengths. If the comparative sensitivity claims were valid, the resource would support a meaningful, falsifiable empirical message about how surface-level changes affect LLM and human treatment decisions. However, as detailed below, the key statistical comparisons for human annotators rest on a pairing assumption that the data collection design explicitly violates, so the headline findings are not currently supported. The paper's own limitations section candidly acknowledges some related concerns, but it does not address the specific unpaired-annotator problem that undermines both case studies.

major comments (3)
  1. [§3.3 and §4.2, Appendix K.3] Section 3.3 states that 'no labeler was provided with two clinical contexts corresponding to the same baseline clinical context.' The Percent Change metric PC_q in Section 4.2 is defined as (1/N)Σ|t_pert_i,q - t_base_i,q|, which requires a baseline–perturbed pair from the same decision-maker i. For humans, no such pairs exist, so the computation must either pair different annotators arbitrarily or use context-level aggregates; neither choice supports the paired t-test whose validity Appendix K.3 justifies by 'the same annotator or model instance.' Consequently, the reported ~30% increase in human self-management recommendations under summaries and the ~20% decrease in resource allocation are not established by the tests as written, and the second headline claim—that human annotators are more sensitive to format perturbations—is unsupported.
  2. [§4.1 and Appendix K.4] The mutual information MI_q is defined through the joint probability P(t_base_q=a, t_pert_q=b) of decisions under baseline and perturbed conditions. For LLMs, this can be computed for the same model instance across sampling seeds; for humans, the disjoint annotator design means the joint distribution is actually a cross-annotator agreement table, not a measure of decision stability. A high or low human MI in this setting reflects inter-annotator (dis)agreement, so the comparison between human and LLM MI does not support the conclusion that 'clinicians tend to produce more stable and internally consistent treatment decisions.' In addition, the Mann–Whitney U test described in Appendix K.4 is underspecified: the main text reports one MI value per treatment question, but a U test requires a sample of MI values per group, and the constitution of these samples is never described. The first headline claim—that LLMs are more sensitive to gender and style perturbations than humans—therefore lacks a valid statistical basis.
  3. [§4.1–4.2] The unit of analysis is ambiguous throughout the case studies. Section 4.1 defines t_i,q as the treatment selected by 'annotator or LLM instance i' and N as the number of prompts, but the dataset provides three clinician reads and twelve LLM reads (four models × three seeds) per prompt. It is never stated whether the analyses use individual reads, model runs, majority votes, or some other aggregation. This ambiguity is load-bearing because both headline claims depend on the paired tests, and without a clear definition of what is being paired, the reported p-values and error bars cannot be verified. The authors should specify the exact observations entering each test, or re-run the analyses with an explicitly defined and appropriate aggregation.
minor comments (5)
  1. [Section 1] The introduction closes with 'We provide the MedPeturb dataset,' which should be corrected to 'MedPerturb.'
  2. [Section 3.1] The format perturbation paragraph contains the typo 'multiturn converesations'; it should read 'multiturn conversations.'
  3. [Appendix E.3] The heading 'Text Analysis' introduces 'how the texts are changed by perturabtion'; this should be 'perturbation.'
  4. [Section 3.4] The notation 'Fleiss’κp' is not defined; the subscript p should be explained or removed, as it is not standard for Fleiss' kappa.
  5. [Appendix N.1.2] The sentence 'We do not that for models, longer summaries tend to receive less visitation recommendations' appears to be missing the word 'note' and should be revised.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper is an empirical measurement study with no fitted-parameter derivation; the only self-citation supplies question wording, not the conclusions.

full rationale

I walked the derivation chain and found no circular step of any of the seven enumerated kinds. The paper's central claims—that LLMs shift more under gender/style perturbations and that human annotators shift more under format perturbations—are empirical comparisons of measured yes/no treatment decisions; there is no fitted parameter, no model whose output is defined in terms of the target result, and no formal derivation that could reduce a prediction to an input. The citation to the authors' prior FAccT paper [50] ('three previously established triage questions [50]') supplies only the wording of the MANAGE/VISIT/RESOURCE questions; it does not encode or force the experimental outcomes, so it is a normal, non-load-bearing self-citation. Two potential concerns were checked and do not constitute circularity. First, the unpaired human annotation design stated in Section 3.3 ('no labeler was provided with two clinical contexts corresponding to the same baseline clinical context') undermines the paired-t and mutual-information statistics, but this is a statistical validity threat, not a definitional or fitted-input reduction; no equation in the paper makes the human sensitivity claim true by construction. Second, Llama-3-8B generated the gender/style perturbations and is also one of the evaluated models, but the perturbation axis is defined by the instruction prompt, not by whether any model's output changed, so the measured sensitivity is an empirical result rather than an artifact of definition. The paper is self-contained against external benchmarks and reports no derivation that is equivalent to its inputs. Score 0.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The central claims rest on research-design assumptions about annotator expertise, content invariance, and statistical validity rather than on fitted parameters or new theoretical entities.

assumptions (4)
  • domain assumption Medical students with some clinical training provide a valid proxy for practicing clinicians in treatment triage.
    Section 3.3 recruits 36 medical students, yet the abstract and results refer to 'clinicians' and 'human experts'; the limitation is acknowledged in Section 6 but the central claims are framed around clinician behavior.
  • domain assumption Gender, style, and format perturbations do not alter clinically relevant content.
    The paper filters gendered conditions (Section 3.1) and uses LLMs to make perturbations, but it does not verify content invariance beyond summary-quality correlations in Appendix N.1.
  • ad hoc to paper The paired statistical tests are valid for comparing baseline and perturbed human decisions.
    Section 3.3 assigns different annotators to baseline and perturbed versions, so the paired t-tests in Section 4.2 and Appendix K.3 assume a pairing that the design does not provide.
  • ad hoc to paper Mutual information values are sampled in a way that permits a Mann-Whitney U test.
    Section 4.1 defines one MI_q per question; Appendix K.4 describes a two-sample test on groups of MI values, but the sampling that produces multiple MI values is not specified.

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Cite this review

Pith. "Pith review of The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making." pith.science (2026). https://pith.science/paper/UNCXMYP2

@misc{pith2026250617163,
  author       = {Pith},
  title        = {Pith review of: The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UNCXMYP2}},
  note         = {Machine review of arXiv:2506.17163}
}
read the original abstract

Clinical robustness is critical to the safe deployment of medical Large Language Models (LLMs), but key questions remain about how LLMs and humans may differ in response to the real-world variability typified by clinical settings. To address this, we introduce MedPerturb, a dataset designed to systematically evaluate medical LLMs under controlled perturbations of clinical input. MedPerturb consists of clinical vignettes spanning a range of pathologies, each transformed along three axes: (1) gender modifications (e.g., gender-swapping or gender-removal); (2) style variation (e.g., uncertain phrasing or colloquial tone); and (3) format changes (e.g., LLM-generated multi-turn conversations or summaries). With MedPerturb, we release a dataset of 800 clinical contexts grounded in realistic input variability, outputs from four LLMs, and three human expert reads per clinical context. We use MedPerturb in two case studies to reveal how shifts in gender identity cues, language style, or format reflect diverging treatment selections between humans and LLMs. We find that LLMs are more sensitive to gender and style perturbations while human annotators are more sensitive to LLM-generated format perturbations such as clinical summaries. Our results highlight the need for evaluation frameworks that go beyond static benchmarks to assess the similarity between human clinician and LLM decisions under the variability characteristic of clinical settings.

Figures

Figures reproduced from arXiv: 2506.17163 by the authors.

Figure 1
Figure 1. Agreement metrics show moderate amounts of agreement for both humans and models for [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Average Treatment Ratio (ATR) for baseline, gender, and style perturbations demonstrate [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Mutual Information (MI) metric shows that human decision-making is less sensitive to [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (12 more)
Figure 4
Figure 4. Figure 4: We highlight how humans are significantly impacted by format perturbations while LLM [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Percent Change (PC) metric shows that clinicians are much more likely to flip treatment [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Screenshots of the Centaur Lab interface for our study. Additional examples of how to [PITH_FULL_IMAGE:figures/full_fig_p024_6.png]
Figure 7
Figure 7. Figure 7: Model agreement comparing the % of contexts where three out of four models agree [PITH_FULL_IMAGE:figures/full_fig_p027_7.png]
Figure 8
Figure 8. Figure 8: Question-level breakdown of which model is the “contrarian" when only three models [PITH_FULL_IMAGE:figures/full_fig_p028_8.png]
Figure 9
Figure 9. Figure 9: Pairwise model agreement percentages split by treatment question. Diagonal entries are [PITH_FULL_IMAGE:figures/full_fig_p028_9.png]
Figure 10
Figure 10. Figure 10: Chart showing aggregated clinician￾model agreement from majority reads across treat￾ment questions Dataset Perturbation MANAGE VISIT RESOURCE r/AskaDocs Baseline 54 70 52 r/AskaDocs Gender-Swapped 50 66 50 r/AskaDocs Gender-Removed 42 70 66 r/AskaDocs Uncertain 62 74 …
Figure 11
Figure 11. Figure 11: These side by side figures illustrate small differences in average clinician and model [PITH_FULL_IMAGE:figures/full_fig_p030_11.png]
Figure 12
Figure 12. Figure 12: Distributions of summarization quality metrics [PITH_FULL_IMAGE:figures/full_fig_p031_12.png]
Figure 13
Figure 13. Figure 13: Correlations between summarization metrics and treatment decisions for clinicians and [PITH_FULL_IMAGE:figures/full_fig_p032_13.png]
Figure 14
Figure 14. Figure 14: Box plots showing the distribution of MANAGE, VISIT, and RESOURCE scores across [PITH_FULL_IMAGE:figures/full_fig_p034_14.png]
Figure 15
Figure 15. Figure 15: Model-level breakdown of how number of turns corresponds to treatment decisions [PITH_FULL_IMAGE:figures/full_fig_p034_15.png]

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.