The Paper has a GitHub, the GitHub has a README, the README has Nothing: Reproducibility Signals for Review Support

Reproducibility policies promise "checkable" medical-imaging science, yet many submissions still ship unverifiable artifacts. An analysis of 3722 MICCAI papers shows code-linking rising from 51.8% to 72.5%, but ~13% of linked repositories are inaccessible or empty. We present paper-snitch, a reviewer-facing decision-support tool that turns these signals into an evidence-grounded report. paper-snitch parses PDFs, resolves and sanity-checks repositories, and applies policy-aware checklists aligned with MICCAI expectations, producing a review-time verifiability score decomposed into interpretable sub-scores plus criterion-linked excerpts and artifacts reviewers can inspect. It never executes untrusted code or attempts GPU-heavy reproduction, focusing instead on bounded, verifiable checks. We compare paper-snitch on 100 randomly sampled MICCAI 2025 papers with human annotators using shared evaluation criteria, indicating that automated, bounded checks can scale reproducibility screening while keeping final decisions with reviewers.