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The study introduces a deliberately difficult dataset of five clinician-authored scenarios across four specialties, each scored with atomic weighted MECE rubrics (184 criteria total). Three frontier LLMs (GPT 5.4, Claude Opus 4.7, Gemini 3.1 Pro) show an inversion of clinical priority: critical high-weight criteria pass only 32.4–41.7%, and 56 of 108 weight-5 criteria satisfy none. Autoraters reproduce expert labels at 92.8–94.7% over 552 criteria, supporting a scalable benchmarking pipeline.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/a-rubric-based-controlled-comparison-of-frontier-language-models-on-expert-authored-clinical-reasoning-tasks/327428/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/a-rubric-based-controlled-comparison-of-frontier-language-models-on-expert-authored-clinical-reasoning-tasks/327428.png","ImageObject",300,407,{"name":92,"@type":93},"Ivy","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-27","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",16,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What evaluation gap does the study target in existing medical benchmarks?","Question",{"text":113,"@type":114},"It focuses on synthesis, prioritisation, and safety reasoning under contradictory evidence and resource constraints—capabilities MCQ benchmarks and many conversational rubrics do not adequately measure.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How is the dataset and scoring rubric constructed?",{"text":118,"@type":114},"Five clinician-authored clinical scenarios are provided across four specialties, and each task is evaluated using atomic, weighted, MECE rubric criteria authored from clinician-drafted golden answers.",{"name":120,"@type":111,"acceptedAnswer":121},"What is the main empirical finding about model performance?",{"text":122,"@type":114},"Models pass trivial low-stakes criteria at high rates but fail critical high-weight criteria; weight-5 critical criteria are satisfied only 32.4–41.7%, with 56 of 108 critical criteria unmet by any model.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},327428,1790221142,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":145,"read_time":146,"preview_page_count":4},549758252649,"https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819","arXiv :2607 .02 175v 1 [ cs .AI] 2 Jul 2026  \nA Rubric-Based Controlled Comparison of Frontier Language Models on Expert-Authored  \nClinical Reasoning Tasks  \nDr. Samiha A. Ismail 1 , Fan X. Chen, Ph.D.2 , Ali Merali3  \n1Prime Analytics Consulting Limited  \n2Talbert House; Thomas More University  \n3MAKZ  \nAbstract  \nMultiple-choice medical benchmarks are increasingly saturated, and recent rubric-based evaluations such as HealthBench have shown that open-ended clinical performance is far from solved – its “Hard” subset top score remains 32% . We present a small, deliberately difficult evaluation dataset of five clinician-authored clinical scenarios spanning four specialties (anaesthesia, internal/family medicine, emergency medicine, and obstetrics), each accompanied by an atomic, weighted, MECE rubric (25–62 criteria per task; 184 criteria total) authored from a clinician-drafted golden answer. We evaluate three frontier models: GPT 5.4, Claude Opus 4.7, and Gemini 3.1 Pro. Mean rubric pass rates were 0.47 (Claude), 0.39 (GPT), and 0.37 (Gemini) . The central finding is an inversion of clinical priority: the highest-weighted (weight-5, critical) criteria passed at only 32.4–41.7%, while low-stakes weight-1 criteria passed at 80–90% . 56 of 108 critical (weight- 5) criteria (52%) were satisfied by no model. Three LLM autoraters reproduced expert met/not-met labelson 92.8–94.7% of 552 graded criteria. We position this as a methods-and-preliminary-findings contribution: the five tasks demonstrate a scalable, defensible pipeline ready to develop into a large-scale benchmark.  \n1. Introduction  \nLarge language models now achieve near-ceiling accuracy on multiple-choice medical question-answering benchmarks. Med-PaLM 2 reached up to 86.5% on the MedQA dataset, an improvement of roughly 19 percentage points over its predecessor (Singhal et al., 2025), and subsequent frontier models have pushed higher still. This saturation has shifted the field toward open-ended, rubric-based evaluation that better reflects real clinical work. HealthBench (Arora et al., 2025), built with 262 physicians who had practiced in 60 countries, evaluated models on 5,000 realistic health conversations scored against 48,562 unique rubric  \ncriteria. Crucially, HealthBench remains unsaturated: on its 1,000-example “Hard” subset, the strongest model evaluated achieved a score of 0.32, compared with 0.60 on HealthBench overall, leaving substantial headroom for the next generation of models (Arora et al., 2025) . In parallel, the AMIE program has used OSCE-style, axis-based human and auto-rater evaluation to study diagnostic dialogue and longitudinal disease management (Tu et al., 2025) .  \nYet a specific capability gap remains under-measured: synthesis, prioritisation, and safety reasoning under contradictory evidence and resource constraints. Real clinical decisions require integrating signals that point in opposite directions (for example; having more than one clinical diagnosis which have conflicting treatments), recognising drug–disease and drug–drug interactions, anticipating downstream resource needs, and making time-bound commitments where the safe action is sometimes to withhold an intervention. MCQ benchmarks cannot probe these behaviours, and even rubric-based conversational benchmarks tend to reward completeness and communication quality more than they penalise the omission of a single decisive, highstakes inference.  \nWe address this gap with a small but methodologically rigorous study. Our contributions are: (1) A clinician-authored evaluation methodology in which practising clinicians write organic, narrative scenarios with synthetic patient data and draft an ideal golden answer from which atomic, weighted, MECE rubric criteria are constructed,(2) a controlled comparison of three frontier models (GPT 5.4, Claude Opus 4.7, Gemini 3.1 Pro) under an identical, deliberately conservative single-turn harness, reported in the terminology of OpenAI’s third-p","cbCaiiLfSMGevmdy","https://ap.wps.com/l/cbCaiiLfSMGevmdy","pdf",161615,13,"English","# Abstract\n# 1. Introduction\n# 2. Related Work","[{\"question\":\"What evaluation gap does the study target in existing medical benchmarks?\",\"answer\":\"It focuses on synthesis, prioritisation, and safety reasoning under contradictory evidence and resource constraints—capabilities MCQ benchmarks and many conversational rubrics do not adequately measure.\"},{\"question\":\"How is the dataset and scoring rubric constructed?\",\"answer\":\"Five clinician-authored clinical scenarios are provided across four specialties, and each task is evaluated using atomic, weighted, MECE rubric criteria authored from clinician-drafted golden answers.\"},{\"question\":\"What is the main empirical finding about model performance?\",\"answer\":\"Models pass trivial low-stakes criteria at high rates but fail critical high-weight criteria; weight-5 critical criteria are satisfied only 32.4–41.7%, with 56 of 108 critical criteria unmet by any model.\"}]","A Rubric-Based Controlled Comparison of Frontier Language Models on Expert-Authored Clinical Reasoning Tasks | PDF",1789977180,33]