# Med Critics Eval

> This benchmark evaluates a model's ability to verify factual accuracy in long-form medical texts by recursively decomposing claims into a verification tree. It probes fine-grained fact-checking across six medical domains, requiring the model to distinguish between factual and deliberately falsified claims while accounting for contextual dependencies. Use when the user wants to benchmark on Med-Critics, or asks about evaluating this task. Reports accuracy.

- Skill: `qhjqhj00/med-critics-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/med-critics-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/med-critics-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/med-critics-eval

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# med-critics-eval

> Iterative Tree Analysis for Medical Critics — Huang et al. (2025) (arXiv:2501.10642, 2025)

## What this evaluates

This benchmark evaluates a model's ability to verify factual accuracy in long-form medical texts by recursively decomposing claims into a verification tree. It probes fine-grained fact-checking across six medical domains, requiring the model to distinguish between factual and deliberately falsified claims while accounting for contextual dependencies.

## Datasets

- **Med-Critics** — total 980; splits: test (980)

## Metrics

- `accuracy` **(primary)** — range: [0, 1]
  - Measures the proportion of correctly verified claims or samples against the ground truth labels. Calculated as the number of correct predictions divided by the total number of evaluated claims.
- `F1@K` — range: [0, 1]
  - Evaluates both precision and recall at the top-K retrieved or verified facts. Computed as the harmonic mean of precision and recall considering only the top-K predictions.

## Input / output format

**Input**: Long-form medical text passages (typically 20-30 sentences) containing atomic claims, with a subset deliberately falsified to simulate misinformation.

**Output**: Binary factuality labels (factual/non-factual or accept/reject) for each extracted atomic claim, organized hierarchically as a verification tree.

## Scoring recipe

```python
def compute_accuracy(predictions, gold_labels):
    correct = sum(1 for p, g in zip(predictions, gold_labels) if p == g)
    return correct / len(gold_labels)

def compute_f1_at_k(top_k_preds, top_k_golds, k):
    tp = sum(1 for p, g in zip(top_k_preds[:k], top_k_golds[:k]) if p == g == 1)
    fp = sum(1 for p, g in zip(top_k_preds[:k], top_k_golds[:k]) if p == 1 and g == 0)
    fn = sum(1 for p, g in zip(top_k_preds[:k], top_k_golds[:k]) if p == 0 and g == 1)
    prec = tp / (tp + fp) if (tp + fp) > 0 else 0
    rec = tp / (tp + fn) if (tp + fn) > 0 else 0
    return 2 * prec * rec / (prec + rec) if (prec + rec) > 0 else 0
```

## Common pitfalls

- Long-form factuality lacks a definitive set of facts, making ground-truth construction and claim extraction highly sensitive to LLM prompts and tokenization.
- Medical facts are highly context-dependent; evaluating claims in isolation often yields false positives, requiring subtree-level contextual grounding.
- The number and nature of extracted claims are unpredictable in generative models, complicating fair comparison and consistent scoring across methods.

## Evidence (verbatim from paper)

> To evaluate our method’s performance in assessing the factuality of long-form medical texts, we employ several key metrics. The accuracy measures the discrepancy between the ground truth and the factual verifications, providing a basic accuracy assessment. We also use the $F_{1}@K$ metric Wei et al. ([2024]), which evaluates both precision and recall.

## Citation

```bibtex
@misc{huang2025iterative,
  title={Iterative Tree Analysis for Medical Critics},
  author={Huang et al. (2025)},
  year={2025},
  note={arXiv:2501.10642}
}
```

- arXiv: 2501.10642