GRAND-ROUNDS

GRAND-ROUNDS (Graded Responses and Annotated Notes for Diagnostic Reasoning on UNstructured Data Sets) is a physician-annotated benchmark for validating LLM judges of open-ended clinical reasoning. GRAND-ROUNDS comprises 9,217 physician scores across 5,250 scored response entries from 160 clinicians and 9 AI models across six tasks, drawn from seven published studies and graded by 11 physicians. This release contains all of GRAND-ROUNDS except the BIDMC emergency-department task (Brodeur et al. 2026), which contains protected health information: 4,339 entries with 7,395 independent physician scores across the five remaining tasks. Released with the paper Scaling Clinical Judgment to Evaluate Medical AI. Website: https://preceptron.net · Code: https://github.com/2v/PrecepTron · Models: https://huggingface.co/collections/tbuckley/preceptron-6a3194798cea4c5d6bc9713f

Benchmarks

Benchmark	Description	Records
management_reasoning	Grey Matters management cases (Goh et al. 2025): free-text management answers scored on case-specific rubrics	2,765
cpc_bond	NEJM clinicopathological conference (CPC) cases: differential diagnoses scored with the 0-5 Bond score	853
r_idea	NEJM Healer cases (Cabral et al. 2024): clinical reasoning documentation scored with the 10-point R-IDEA rubric	312
diagnostic_reasoning	Landmark diagnostic cases (Goh et al. 2024): structured diagnostic reasoning scored on the 19-point rubric	278
cpc_management	NEJM CPC cases: proposed diagnostic testing plans scored on the 0-2 testing-plan rubric	131

Files

• data/train-*.parquet: the benchmark, loaded by datasets.load_dataset("tbuckley/GRAND-ROUNDS").
• combined_dataset.json: the same records as one JSON list. The code at https://github.com/2v/PrecepTron reads this file from score_data/combined_dataset.json; the public repo does not ship it, so fetch it there with hf download tbuckley/GRAND-ROUNDS combined_dataset.json --repo-type dataset --local-dir score_data after hf auth login.

Common Fields

• benchmark: Source benchmark identifier
• case_id: Case identifier
• model: AI model or human participant group
• study: Source study citation key
• response: Free-text response from model or participant
• grade: JSON string — list of grader scores (grader, score, and benchmark-specific fields)
• final_diagnosis: Ground truth diagnosis (where applicable)

Fields present only in some benchmarks: question_number, question_text, max_score, run_number, participant_id, asked_together, aliquot, cannot_miss_diagnoses, cannot_miss_score, dataset, questions_raw, test_plan, case_vignette_multi.

case_vignette_multi (management_reasoning only) is a JSON string containing an ordered list of aliquots, each {"vignette": "<cumulative case text visible at this point>", "questions": ["q1", ...]}. Use it to recover the incremental-disclosure vignette that was visible when each question was asked; the flat case_vignette field remains the full concatenation of every aliquot for backward compatibility.

Case text availability

Full case text (case_vignette) is included for management_reasoning (the Grey Matters cases), released here for the first time. The Landmark Diagnostic Cases (diagnostic_reasoning) ship with responses, physician scores, and final diagnoses but not the case presentations, and the NEJM CPC and NEJM Healer case presentations are copyrighted by the publisher, so diagnostic_reasoning, cpc_bond, cpc_management, and r_idea carry no case text (is_case_released is False on those rows). Note that the PrecepTron judge for diagnostic_reasoning reads the case vignette, so that task's judge benchmark cannot be re-run from this release alone. The BIDMC emergency-department task reported in the paper is not included (patient-derived data).

Citation

@article{buckley2026preceptron,
  title   = {Scaling Clinical Judgment to Evaluate Medical AI},
  author  = {Buckley, Thomas A. and Kanjee, Zahir and Brodeur, Peter G. and
             Crowe, Byron and Pettinato, Anthony M. and Shah, Aashna P. and
             Haimovich, Adrian D. and McCoy, Liam G. and Restrepo, Daniel and
             Freed, Jason A. and Goh, Ethan and Chen, Jonathan H. and Zwaan, Laura and
             Goodman, Katherine E. and Morgan, Daniel J. and
             Abdulnour, Raja-Elie E. and Rodman, Adam and Manrai, Arjun K.},
  year    = {2026},
  journal = {arXiv preprint arXiv:2609.12822},
  url     = {https://arxiv.org/abs/2609.12822}
}