MedSci Skills v4.4.0

Added

• /peer-review + /self-review — Image-Synthesis / Cross-Modality Generation probe module (IS1–IS4) + reviewer-side reference-integrity spot-check. New domain-probe module image_synthesis.md (vendored byte-identical into /self-review; MODULES 10 → 11, sync gate updated) for studies that synthesize one imaging modality from another (MRI→PET / MRI→CT / non-contrast→contrast / low-dose→full-dose) and claim the output carries functional/molecular information or substitutes for the unavailable target. IS1 determinism/information-ceiling (the synthetic image is a deterministic function of the source, so a same-reader "source + synthetic > source alone" gain is a presentation/interpretability effect absent a direct source→label baseline); IS2 target-derived-preprocessing / undescribed slice-selection leakage (a lesion mask drawn on the target modality guiding slice selection or training makes "function inferred from structure" circular — undescribed provenance is itself a Major #1 candidate); IS3 global-vs-lesion-level quantitative agreement (whole-organ SUVR agreement does not establish lesion-level fidelity); IS4 mechanistic/proxy-signal plausibility (name what the source physically measures vs the target's biology — high image similarity is not evidence an unmeasured signal was recovered). Routed from a new peer-review Phase 2K + Phase 3 QC item 15 + Phase 5 routing line, and a /self-review routing-table row. Per Phase 2F, IS2/IS4 are typically unfixable-in-current-form and govern the recommendation toward Reject-leaning. Companion reviewer-side reference-integrity spot-check added to the Phase 2 issue checklist + Phase 3 QC item 16 (all original-research reviews): spot-check the load-bearing Introduction/Discussion citations used as evidence the method/premise works — a paper cited for a different task, a duplicate reference, a wrong year/author — phrasing unconfirmed suspicions "please verify" (the reviewer-side mirror of the authoring citation-safety discipline). Motivation: a decision-audit of a cross-modality MRI→synthetic-PET reader-study review where the three structurally distinct synthesis failure modes were split across reviewers and the reference-list errors went uncaught on the reviewer side.
• /author-strategy — trajectory-archetype classification (optional, explainable multi-label heuristic). Adds an opt-in capability that classifies a queried author's PubMed trajectory into abstract career archetypes (A1 infrastructure builder, A2 methodology rule-maker, A3 clinical→AI hybrid, A4 SR/MA volume engine, A5 large-consortium participation pattern, A6 clinical-subspecialty device/technique depth, plus a computed A3+A6 composite). The rubric is a single canonical data file (references/trajectory_archetypes.yaml); the narrative references/trajectory_archetypes.md is generated from it by render_archetype_doc.py (--check gate). Each label carries a 0–1 score (computable-signal-weight denominator; unavailable signals — h-index/citation/venue-tier — are excluded and surfaced as [VERIFY], never fabricated), a confidence band capped per archetype, and evidence drawn from the author's own PMIDs (evidence_pmids for per-paper signals, evidence_summary for corpus-level); a negative rule suppresses a label to insufficient evidence. A disambiguation gate precedes classification: fetch_pubmed.py writes a corpus_manifest.json cryptographically bound to the CSV (csv_sha256 + pmid_set_hash) and classify_archetypes.py refuses to run unless review_status: approved and the hashes match — a surname alone never resolves an author, and --approve is a human gate. Target-author attribution (ORCID/affiliation/initials/position) is split into a stdlib-only pubmed_parse.py and never borrows a co-author's metadata on a same-surname collision; author position is reported as a first/middle/last/unknown positional heuristic (not leadership metadata), and analyze_patterns.py's "Leadership rate" is renamed "First/last positional rate". The output header states the labels are explainable heuristics, not objective classifications. Ships name-free synthetic fixtures + a CI-gated regression test (A14). Skill count unchanged — an enhancement, not a new skill.
• /verify-refs — OpenAlex tertiary index (conference-proceedings / non-DOI recovery). PubMed covers only biomedical literature and CrossRef's proceedings coverage is uneven, so NeurIPS / ICLR / ACL-style citations — common in medical-AI manuscripts — fall through both and were marked UNVERIFIED. After the PubMed and CrossRef tiers, verify_refs.py now consults OpenAlex (https://api.openalex.org, free, no API key) only when no authoritative author list was obtained yet (a reference already resolved by PubMed/CrossRef incurs no extra call). It resolves by DOI when present, otherwise by a token-similarity-guarded title search so a fabricated title cannot earn a spurious OK. This is the free analogue of the second index (e.g. Scopus) that journal portals run alongside CrossRef. Because OpenAlex display names carry no structured family/given field and mix First Last with Last, First forms, OpenAlex-sourced authors support an existence check plus a tolerant first-author membership check but never drive the strict positional or author-count MISMATCH (reserved for PubMed efetch / CrossRef); an OpenAlex miss is UNVERIFIED, never FABRICATED. New --no-openalex flag restricts verification to PubMed + CrossRef. Ships a network-free regression test (tests/test_openalex_tier.sh, monkeypatched http_json, CI gate A8b). Motivation: a medical-AI reference list where two NeurIPS citations validated on Scopus but not CrossRef in a journal portal's reference check.