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Metric Hub

Accuracy + Medicine Metric Papers

Updated from current HFEPX corpus (Feb 27, 2026). 27 papers are grouped in this metric page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Freeform. Frequently cited benchmark: Retrieval. Common metric signal: accuracy. Use this page to compare protocol setup, judge behavior, and labeling design decisions before running new eval experiments. Newest paper in this set is from Feb 26, 2026.

Papers: 27 Last published: Feb 26, 2026 Global RSS

Research Narrative

Grounded narrative Model: deterministic-grounded Source: persisted

Updated from current HFEPX corpus (Feb 27, 2026). This page tracks 27 papers for Accuracy + Medicine Metric Papers. Dominant protocol signals include automatic metrics, simulation environments, with frequent benchmark focus on Retrieval, Banglasummeval and metric focus on accuracy, cost. Use the grounded sections below to prioritize reproducible protocol choices, benchmark-matched comparisons, and judge-vs-human evaluation checks.

Why This Matters For Eval Research

Protocol Takeaways

Benchmark Interpretation

  • Retrieval appears in 18.5% of hub papers (5/27); use this cohort for benchmark-matched comparisons.
  • Banglasummeval appears in 3.7% of hub papers (1/27); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 100% of hub papers (27/27); compare with a secondary metric before ranking methods.
  • cost is reported in 18.5% of hub papers (5/27); compare with a secondary metric before ranking methods.

Researcher Checklist

  • Close gap on Papers with explicit human feedback. Coverage is a replication risk (22.2% vs 45% target).
  • Close gap on Papers reporting quality controls. Coverage is a replication risk (0% vs 30% target).
  • Tighten coverage on Papers naming benchmarks/datasets. Coverage is usable but incomplete (25.9% vs 35% target).
  • Maintain strength on Papers naming evaluation metrics. Coverage is strong (100% vs 35% target).
  • Maintain strength on Papers with known rater population. Coverage is strong (44.4% vs 35% target).
  • Close gap on Papers with known annotation unit. Coverage is a replication risk (11.1% vs 35% target).

Papers with explicit human feedback

Coverage is a replication risk (22.2% vs 45% target).

Papers reporting quality controls

Coverage is a replication risk (0% vs 30% target).

Papers naming benchmarks/datasets

Coverage is usable but incomplete (25.9% vs 35% target).

Papers naming evaluation metrics

Coverage is strong (100% vs 35% target).

Papers with known rater population

Coverage is strong (44.4% vs 35% target).

Papers with known annotation unit

Coverage is a replication risk (11.1% vs 35% target).

Suggested Reading Order

  1. 1. Strategy Executability in Mathematical Reasoning: Leveraging Human-Model Differences for Effective Guidance

    Start here for detailed protocol reporting, including rater and quality-control evidence.

  2. 2. Importance of Prompt Optimisation for Error Detection in Medical Notes Using Language Models

    Start here for detailed protocol reporting, including rater and quality-control evidence.

  3. 3. MEDSYN: Benchmarking Multi-EviDence SYNthesis in Complex Clinical Cases for Multimodal Large Language Models

    Start here for detailed protocol reporting, including rater and quality-control evidence.

  4. 4. SurGo-R1: Benchmarking and Modeling Contextual Reasoning for Operative Zone in Surgical Video

    Adds automatic metrics with expert verification for broader coverage within this hub.

  5. 5. Virtual Biopsy for Intracranial Tumors Diagnosis on MRI

    Adds automatic metrics for broader coverage within this hub.

  6. 6. XMorph: Explainable Brain Tumor Analysis Via LLM-Assisted Hybrid Deep Intelligence

    Adds automatic metrics for broader coverage within this hub.

  7. 7. OrthoDiffusion: A Generalizable Multi-Task Diffusion Foundation Model for Musculoskeletal MRI Interpretation

    Adds automatic metrics for broader coverage within this hub.

  8. 8. MedCLIPSeg: Probabilistic Vision-Language Adaptation for Data-Efficient and Generalizable Medical Image Segmentation

    Adds automatic metrics for broader coverage within this hub.

Known Limitations

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Annotation unit is under-specified (11.1% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.

Research Utility Links

automatic_metrics vs simulation_env

both=1, left_only=26, right_only=0

1 papers use both Automatic Metrics and Simulation Env.

Benchmark Brief

Retrieval

Coverage: 5 papers (18.5%)

5 papers (18.5%) mention Retrieval.

Examples: Strategy Executability in Mathematical Reasoning: Leveraging Human-Model Differences for Effective Guidance , AgenticRAGTracer: A Hop-Aware Benchmark for Diagnosing Multi-Step Retrieval Reasoning in Agentic RAG , Evidence-Grounded Subspecialty Reasoning: Evaluating a Curated Clinical Intelligence Layer on the 2025 Endocrinology Board-Style Examination

Benchmark Brief

Banglasummeval

Coverage: 1 papers (3.7%)

1 papers (3.7%) mention Banglasummeval.

Examples: BanglaSummEval: Reference-Free Factual Consistency Evaluation for Bangla Summarization

Benchmark Brief

MedMCQA

Coverage: 1 papers (3.7%)

1 papers (3.7%) mention MedMCQA.

Examples: To Reason or Not to: Selective Chain-of-Thought in Medical Question Answering

Metric Brief

accuracy

Coverage: 27 papers (100%)

27 papers (100%) mention accuracy.

Examples: Strategy Executability in Mathematical Reasoning: Leveraging Human-Model Differences for Effective Guidance , Importance of Prompt Optimisation for Error Detection in Medical Notes Using Language Models , MEDSYN: Benchmarking Multi-EviDence SYNthesis in Complex Clinical Cases for Multimodal Large Language Models

Metric Brief

cost

Coverage: 5 papers (18.5%)

5 papers (18.5%) mention cost.

Examples: To Reason or Not to: Selective Chain-of-Thought in Medical Question Answering , Continuous Telemonitoring of Heart Failure using Personalised Speech Dynamics , BanglaSummEval: Reference-Free Factual Consistency Evaluation for Bangla Summarization

Metric Brief

recall

Coverage: 3 papers (11.1%)

3 papers (11.1%) mention recall.

Examples: To Reason or Not to: Selective Chain-of-Thought in Medical Question Answering , BanglaSummEval: Reference-Free Factual Consistency Evaluation for Bangla Summarization , Glycemic-Aware and Architecture-Agnostic Training Framework for Blood Glucose Forecasting in Type 1 Diabetes

Most Cited In This Hub

Fast path to methods with the strongest citation traction in this scope.

Papers: Strategy Executability in Mathematical Reasoning: Leveraging Human-Model Differences for Effective Guidance , Importance of Prompt Optimisation for Error Detection in Medical Notes Using Language Models , MEDSYN: Benchmarking Multi-EviDence SYNthesis in Complex Clinical Cases for Multimodal Large Language Models

Most Recent

Fast path to latest protocol changes and newly published evaluation setups.

Papers: Strategy Executability in Mathematical Reasoning: Leveraging Human-Model Differences for Effective Guidance , Importance of Prompt Optimisation for Error Detection in Medical Notes Using Language Models , MEDSYN: Benchmarking Multi-EviDence SYNthesis in Complex Clinical Cases for Multimodal Large Language Models

Best Protocol Detail

Papers with explicit rater/unit metadata and quality-control signals for reproducibility.

Papers: Strategy Executability in Mathematical Reasoning: Leveraging Human-Model Differences for Effective Guidance , Importance of Prompt Optimisation for Error Detection in Medical Notes Using Language Models , MEDSYN: Benchmarking Multi-EviDence SYNthesis in Complex Clinical Cases for Multimodal Large Language Models

Top Papers Reporting This Metric

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