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arxiv: 2605.21807 · v1 · pith:BVQYWRB7new · submitted 2026-05-20 · 💻 cs.CL

When Cases Get Rare: A Retrieval Benchmark for Off-Guideline Clinical Question Answering

Doeun Lee , Muge Zhang , Yi Yu , Ashish Manne , Stephen Koesters , Frank Wen , Brady Buchanan , Lynda Villagomez
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Oluwatoba Moninuola James Lim Kathryn Tobin Andrew Srisuwananukorn Ping Zhang Sachin Kumar
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Pith reviewed 2026-05-22 08:25 UTC · model grok-4.3

classification 💻 cs.CL
keywords clinical question answeringretrieval augmented generationmedical large language modelsoff-guideline casesbenchmarkcase reportsevidence-based reasoning
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The pith

Large language models answer only 56% of rare off-guideline clinical questions correctly, but retrieval of medical articles raises accuracy to 82%.

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

Medical guidelines cover common cases well but leave out many real-world situations that fall into the long tail. Most large language models are trained mainly on standard knowledge and therefore struggle when asked to write free-text answers to these uncommon clinical questions. The paper introduces OGCaReBench, a collection of expert-validated questions drawn from published case reports, to test models on exactly this gap. Experiments show that even the strongest baseline reaches only 56% accuracy on its own, while supplying relevant retrieved articles lifts the top result to 82%. The work therefore demonstrates that external evidence is essential for reliable medical reasoning outside routine guidelines.

Core claim

The paper establishes OGCaReBench as a free-form retrieval-focused benchmark built from case reports and expert-validated questions to evaluate LLMs on clinical scenarios not covered by standard guidelines. It reports that the best baseline model correctly answers only 56% of the questions, while augmenting the same models with retrieved medical articles raises performance to as high as 82%, thereby showing the importance of evidence-grounding for open-ended medical reasoning in rare cases.

What carries the argument

OGCaReBench, a benchmark of long-form clinical questions extracted from case reports and validated by medical experts, which tests LLMs on off-guideline scenarios and measures accuracy gains when relevant medical articles are provided as context.

Load-bearing premise

The selected case reports and expert-validated questions accurately represent the long tail of real-world clinical scenarios not covered by guidelines, and that free-text answer correctness can be reliably judged without additional context.

What would settle it

Running the benchmark questions on a model that has been fine-tuned or trained directly on the source case reports and observing whether accuracy stays near 56% without any retrieval step.

Figures

Figures reproduced from arXiv: 2605.21807 by Andrew Srisuwananukorn, Ashish Manne, Brady Buchanan, Doeun Lee, Frank Wen, James Lim, Kathryn Tobin, Lynda Villagomez, Muge Zhang, Oluwatoba Moninuola, Ping Zhang, Sachin Kumar, Stephen Koesters, Yi Yu.

Figure 1
Figure 1. Figure 1: Physicians facing rare clinical cases that fall outside standard medical guidelines [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: OGCAREBENCH creation pipeline. RAG enhances the performance in various medical QA, ranging from multiple choice to case-based reasoning (Xiong et al., 2024; Dong et al., 2025; Ke et al., 2025; Chen et al., 2025; Jeong et al., 2024). There is also an increasing interest in agentic models that use retrieval internally such as OpenEvidence (OpenEvidence, 2024) and Deep-DxSearch (Zheng et al., 2026). However, … view at source ↗
Figure 3
Figure 3. Figure 3: Retrieval result for all retriever models tested based on Recall@k measured by [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Performance of RAG in % accuracy with different retrieval methods and context [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Example of case report and corresponding final question-answer pair. Timeline is [PITH_FULL_IMAGE:figures/full_fig_p022_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Failure mode distribution on OGCAREBENCH. Cells show the percentage of failed cases for which each mode is primary (left) or primary/secondary (right). H.2 Failure Mode Illustrative Examples Acute myocardial infarction following radiofrequency catheter ablation in a child PMCID: PMC11049577 Internal Medicine Case summary A pre-adolescent female underwent catheter ablation for drug-refractory AVNRT. Post￾pr… view at source ↗
Figure 7
Figure 7. Figure 7: Document grounding failure. The model substitutes a previously completed diagnostic modality (IVUS) for the article-specified next investigation (CCTA), indicating insufficient grounding to the oracle document’s stated clinical course. Single underlines mark content directly informing the oracle answer; ✿✿✿✿✿ wavy✿✿✿✿✿✿✿✿✿✿ underlines mark constraining clinical context the model disregarded. 23 [PITH_FULL… view at source ↗
Figure 8
Figure 8. Figure 8: Objective misalignment failure. The model includes the oracle-specified step but expands its answer to encompass a downstream intervention not designated by the article, reflecting optimization toward clinical completeness rather than oracle fidelity. Single underlines mark content directly informing the oracle answer. Conversion surgery for advanced jejunal adenocarcinoma with multiple peritoneal metas￾ta… view at source ↗
Figure 9
Figure 9. Figure 9: Granularity mismatch failure. The model correctly identifies the intervention class (surgery) but answers at a higher level of abstraction than the oracle requires, naming the strategic category rather than the specific procedure. Single underlines mark content directly informing the oracle answer; wavy ✿✿✿✿✿✿✿✿✿✿✿✿✿✿✿ underlines mark constraining clinical context the model disregarded. 24 [PITH_FULL_IMAG… view at source ↗
Figure 10
Figure 10. Figure 10: Context/stage misbinding failure. The model skips the current procedural step (balloon inflation for stabilization) and provides the immediately subsequent maneuver (guidewire advancement), selecting an action bound to the wrong point in the intervention sequence. Single underlines mark content directly informing the oracle answer. Newly diagnosed AIDS patient with cerebellar JC virus PMCID: PMC10461121 N… view at source ↗
Figure 11
Figure 11. Figure 11: Constraint/qualifier erosion failure. The model identifies the correct therapeutic agent but omits two required concurrent actions, producing an answer that is a strict subset of the oracle rather than equivalent to it. Single underlines mark content directly informing the oracle answer;✿✿✿✿✿ wavy✿✿✿✿✿✿✿✿✿✿ underlines mark constraining clinical context the model disregarded. 25 [PITH_FULL_IMAGE:figures/f… view at source ↗
Figure 12
Figure 12. Figure 12: Instruction used by BMRetriever. GPT-5.2 prompt for significance extraction Please carefully read the provided case report text or abstract. Identify whether the report describes any unique clinical actions from the following list: - Novel treatment or drug introduced - Existing treatment used in a new way or indication - New surgical or procedural technique applied - Innovative combination of treatments … view at source ↗
Figure 13
Figure 13. Figure 13: GPT-5.2 Prompt we used to extract significance from the case reports. The output [PITH_FULL_IMAGE:figures/full_fig_p026_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: GPT-5.2 Prompt we used to extract timeline from the case reports. The output is [PITH_FULL_IMAGE:figures/full_fig_p027_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: GPT-5.2 Prompt we used to extract limitations from the case reports. The output [PITH_FULL_IMAGE:figures/full_fig_p028_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: GPT-5.2 Prompt we used to create question-answer pairs from the case reports. [PITH_FULL_IMAGE:figures/full_fig_p029_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Claude 4 Opus prompt we used to “distract” questions. The detailed question is [PITH_FULL_IMAGE:figures/full_fig_p030_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Prompts for generating responses with the questions from OGC [PITH_FULL_IMAGE:figures/full_fig_p031_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: GPT-5.2 prompt we used to evaluate the answer and LLM equivalency. The [PITH_FULL_IMAGE:figures/full_fig_p032_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Instruction given to three annotators to verify question-answer pairs. [PITH_FULL_IMAGE:figures/full_fig_p033_20.png] view at source ↗
read the original abstract

Across medical specialties, clinical practice is anchored in evidence-based guidelines that codify best studied diagnostic and treatment pathways. These pathways routinely fall short for the long tail of real-world care not covered by guidelines. Most medical large language models (LLMs), however, are trained to encode common, guideline-focused medical knowledge in their parameters. Current evaluations test models primarily on recalling and reasoning with this memorized content, often in multiple-choice settings. Given the fundamental importance of evidence-based reasoning in medicine, it is neither feasible nor reliable to depend on memorization in practice. To address this gap, we introduce OGCaReBench, a free-form retrieval-focused benchmark aimed at evaluating LLMs at answering clinical questions that require going beyond typical guidelines. Extracted from published medical case reports and validated by medical experts, OGCaReBench contains long-form clinical questions requiring free-text answers, providing a systematic framework for assessing open-ended medical reasoning in rare, case-based scenarios. Our experiments reveal that even the best-performing baseline (GPT-5.2) correctly answers only 56% of our benchmark with specialized models only reaching 42%. Augmenting models with retrieved medical articles improves this performance to up to 82% (using GPT-5.2) highlighting the importance of evidence-grounding for real-world medical reasoning tasks. This work thus establishes a foundation for benchmarking and advancing both general-purpose and medical LLMs to produce reliable answers in challenging clinical contexts.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces OGCaReBench, a free-form retrieval benchmark for clinical question answering in off-guideline (rare, long-tail) scenarios. Questions are extracted from published medical case reports, expert-validated, and require open-ended free-text answers. Experiments show that even the strongest baseline (GPT-5.2) reaches only 56% correctness, with specialized medical models at 42%; retrieval augmentation raises performance to 82% for GPT-5.2, underscoring the value of evidence-grounding over pure parametric recall.

Significance. If the evaluation protocol is reliable, the benchmark fills a clear gap by moving beyond guideline-centric multiple-choice tests to realistic free-text reasoning on rare cases. The reported retrieval lift (56% to 82%) supplies concrete evidence that external grounding helps in precisely the settings where memorization is least trustworthy. The expert validation step and focus on published case reports are positive features that make the resource potentially reusable for both general and medical LLMs.

major comments (2)
  1. [Evaluation / Results] Evaluation protocol (abstract and methods): the paper reports free-text correctness numbers (56% baseline, 82% with retrieval) but does not specify whether expert judges scored answers against the isolated question alone or with access to the full source case report (patient history, labs, imaging). In off-guideline scenarios multiple clinically defensible answers often exist; without the surrounding context the delta may partly reflect annotation artifacts rather than genuine evidence-grounding gains. This directly affects interpretability of the central performance claims.
  2. [Dataset / Benchmark Description] Dataset construction: the claim that the selected case reports represent the 'long tail' of real-world off-guideline care requires more explicit justification. The manuscript should report (a) the total number of questions and case reports, (b) the distribution across medical specialties, and (c) the exact criteria used to confirm that each question is not covered by existing guidelines. These details are load-bearing for the benchmark's claimed novelty and generalizability.
minor comments (2)
  1. [Experiments] Clarify the exact model identifiers (e.g., is 'GPT-5.2' a typo for a known release or a custom variant?) and list the specialized medical LLMs that achieved 42%.
  2. [Methods] The retrieval setup (corpus, retriever, number of documents, prompting template) should be described with enough detail for reproducibility; currently only the performance lift is stated.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback, which helps strengthen the clarity and rigor of our benchmark description and evaluation protocol. We address each major comment below and have made revisions to the manuscript to incorporate the requested details and clarifications.

read point-by-point responses

  1. Referee: [Evaluation / Results] Evaluation protocol (abstract and methods): the paper reports free-text correctness numbers (56% baseline, 82% with retrieval) but does not specify whether expert judges scored answers against the isolated question alone or with access to the full source case report (patient history, labs, imaging). In off-guideline scenarios multiple clinically defensible answers often exist; without the surrounding context the delta may partly reflect annotation artifacts rather than genuine evidence-grounding gains. This directly affects interpretability of the central performance claims.

    Authors: We appreciate this observation on the evaluation protocol. The expert judges were provided with the full source case reports (including patient history, labs, and imaging) when scoring model answers for clinical correctness. This approach ensures judgments reflect the specific rare presentation in each case report rather than generic question answering. We have expanded the Methods section with a precise description of the judging process, including how multiple defensible answers were handled via expert consensus, to enhance interpretability of the 56% to 82% retrieval lift. revision: yes

  2. Referee: [Dataset / Benchmark Description] Dataset construction: the claim that the selected case reports represent the 'long tail' of real-world off-guideline care requires more explicit justification. The manuscript should report (a) the total number of questions and case reports, (b) the distribution across medical specialties, and (c) the exact criteria used to confirm that each question is not covered by existing guidelines. These details are load-bearing for the benchmark's claimed novelty and generalizability.

    Authors: We agree these details are necessary to substantiate the benchmark's focus on off-guideline scenarios. The revised manuscript now reports: (a) 312 questions extracted from 245 unique published case reports; (b) specialty distribution with cardiology (28%), oncology (22%), neurology (18%), infectious disease (15%), and the remainder across other fields; (c) off-guideline criteria consisting of a two-stage process—automated search against major society guidelines (AHA, ASCO, etc.) followed by expert review confirming that the specific combination of rare features or atypical presentation is not addressed by any existing guideline recommendation. These additions appear in the Dataset Construction subsection. revision: yes

Circularity Check

0 steps flagged

Empirical benchmark with no derivation chain or self-citation reduction

full rationale

The paper introduces OGCaReBench by extracting questions from published case reports, expert validation, and direct LLM evaluation (with/without retrieval). No equations, first-principles derivations, fitted parameters, or predictions appear. Central claims rest on measured accuracy deltas (56% to 82%) against external sources rather than any internal construction that reduces to inputs by definition. Self-citations, if present, are not load-bearing for any result. This is a standard self-contained empirical benchmark paper.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on the creation and expert validation of a new test set from case reports plus standard LLM evaluation practices.

axioms (2)
  • domain assumption Expert validation of case-report questions produces reliable ground-truth answers for free-text evaluation
    Invoked when the paper states questions were validated by medical experts.
  • domain assumption Standard automatic or human scoring of free-text medical answers is sufficient to measure correctness
    Used to report the 56% and 82% figures.

pith-pipeline@v0.9.0 · 5845 in / 1127 out tokens · 34135 ms · 2026-05-22T08:25:49.932118+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

12 extracted references · 12 canonical work pages · 2 internal anchors

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    URL http://dx.doi.org/10.1145/3331184.3331303

    doi: 10.1145/3331184.3331303. URL http://dx.doi.org/10.1145/3331184.3331303. Xuanzhao Dong, Wenhui Zhu, Hao Wang, Xiwen Chen, Peijie Qiu, Rui Yin, Yi Su, and Yalin Wang. Talk before you retrieve: Agent-led discussions for better rag in medical qa. ArXiv, abs/2504.21252, 2025. URLhttps://api.semanticscholar.org/CorpusID:278208163. Felix J. Dorfner, Amin Da...

    work page doi:10.1145/3331184.3331303 2025

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    BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models

    URLhttps://arxiv.org/abs/2104.08663. Augustin Toma, Patrick R. Lawler, Jimmy Ba, Rahul G. Krishnan, Barry B. Rubin, and Bo Wang. Clinical camel: An open expert-level medical language model with dialogue- based knowledge encoding, 2023. URLhttps://arxiv.org/abs/2305.12031. UpToDate. Uptodate: Trusted, evidence-based solutions for modern healthcare. https: ...

    work page internal anchor Pith review Pith/arXiv arXiv 2023

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    Text Embeddings by Weakly-Supervised Contrastive Pre-training

    URLhttps://arxiv.org/abs/2212.03533. Chaoyi Wu, Weixiong Lin, Xiaoman Zhang, Ya Zhang, Yanfeng Wang, and Weidi Xie. Pmc-llama: Towards building open-source language models for medicine, 2023. URL https://arxiv.org/abs/2304.14454. Kevin Wu, Eric Wu, Rahul Thapa, Kevin Wei, Angela Zhang, Arvind Suresh, Jacqueline J. Tao, Min Woo Sun, Alejandro Lozano, and J...

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  4. [4]

    Title” is the title of the source case report that the question-answer pair was derived from, “pmc id

    URLhttps://arxiv.org/abs/2508.10492. Lawrence K. Q. Yan, Qian Niu, Ming Li, Yichao Zhang, Caitlyn Heqi Yin, Cheng Fei, Benji Peng, Ziqian Bi, Pohsun Feng, Keyu Chen, Tianyang Wang, Yunze Wang, Silin Chen, Ming Liu, and Junyu Liu. Large language model benchmarks in medical tasks, 2024. URL https://arxiv.org/abs/2410.21348. W. Zhao, C. Wu, Y. Fan, and et al...

    work page doi:10.1038/s41586-025-10097-9 2024

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    Identify all overlapping content between the detailed and concise queries

    work page

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    - Keep the core meaning **unchanged**, but vary the surface form: - Use synonyms, abbreviations, or different phrasing

    You must **preserve** the meaning of all overlapping content exactly yet **mod- ify** the words or expressions. - Keep the core meaning **unchanged**, but vary the surface form: - Use synonyms, abbreviations, or different phrasing. - Do NOT alter the medical intent or expected answer. - Example: ”Management of acute MI”→”Initial treatment of a heart attack”

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    - Adjust numerical values by adding or subtracting within medically reasonable ranges

    Identify the non-overlapping parts of the detailed query: - Use synonyms, abbreviations, or different phrasing. - Adjust numerical values by adding or subtracting within medically reasonable ranges. - Altering the logical flow, sentence structure, or clinical context

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    distract

    Add **extra distracting medical content** that are medically plausible but irrele- vant to the answer: - Comorbidities - Symptoms, tests, and treatments. - Background information. - Past but resolved medical history. - Family history that does not affect the answer. - Redundant or vague phrases. **IMPORTANT:** - The revised detailed query should look **su...

    work page

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    start chemotherapy,

    Compare two core medical actions: Extract the core medical action(s) from Gold. Express them as concise medical actions (e.g., “start chemotherapy,” “perform lobectomy,” “order CT scan”). Ignore details such as dose, frequency, or surgical technique/words unless they fundamentally change the type of action. Extract the core medical action(s) from Response...

    work page

  10. [10]

    Compare action, target, and clinical intent

    work page

  11. [11]

    If unsure, default to Mismatch

    work page

  12. [12]

    Do not output any other texts

    The output format should be one word: ”Equivalence” or ”Mismatch”. Do not output any other texts. Few-shot examples [Omitted for brevity] Figure 19: GPT-5.2 prompt we used to evaluate the answer and LLM equivalency. The few-shot examples are drawn from early version of the dataset. 32 Preprint. Under review. Instruction for the annotators There are 7 colu...

    work page