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Total papers: 3 Search mode: keyword Shortlist (0) RSS

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Citations: 0

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 98% Moderate protocol signal Freshness: Cold Status: Ready
Rubric RatingExpert Verification Automatic Metrics Medicine
  • Validated in sports rehabilitation, we release a knowledge graph (357,844 nodes, 371,226 edges) and a benchmark of 1,637 QA pairs.
  • Five expert clinicians rated the system 4.66--4.84 on a 5-point Likert scale, and system rankings are preserved on a human-verified gold subset (n=80).
Open paper
A Scalable Framework for Evaluating Health Language Models

Neil Mallinar, A. Ali Heydari, Xin Liu, Anthony Z. Faranesh, Brent Winslow, Nova Hammerquist · Mar 30, 2025

Citations: 0

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 98% High protocol signal Freshness: Cold Status: Ready
Rubric RatingExpert Verification Automatic Metrics Medicine
  • As LLM-driven health applications are increasingly adopted, rigorous and efficient one-sided evaluation methodologies are crucial to ensure response quality across multiple dimensions, including accuracy, personalization and safety.
  • In this work, we introduce Adaptive Precise Boolean rubrics: an evaluation framework that streamlines human and automated evaluation of open-ended questions by identifying gaps in model responses using a minimal set of targeted rubrics…
Open paper
PanCanBench: A Comprehensive Benchmark for Evaluating Large Language Models in Pancreatic Oncology

Yimin Zhao, Sheela R. Damle, Simone E. Dekker, Scott Geng, Karly Williams Silva, Jesse J Hubbard · Mar 2, 2026

Citations: 0

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 98% High protocol signal Freshness: Cold Status: Fallback
Rubric RatingExpert Verification Llm As JudgeAutomatic Metrics Medicine
  • Large language models (LLMs) have achieved expert-level performance on standardized examinations, yet multiple-choice accuracy poorly reflects real-world clinical utility and safety.
  • We evaluated 22 proprietary and open-source LLMs using an LLM-as-a-judge framework, measuring clinical completeness, factual accuracy, and web-search integration.
Open paper

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