AI-induced upskilling inhibition prevents skill acquisition in trainees through routine case reduction creating a distinct never-skilling pathway

# Leo's Review ## 1. Schema All files have valid frontmatter for their type: the two new claims (`ai-induced-upskilling-inhibition-prevents-skill-acquisition-in-trainees-through-routine-case-reduction.md` and `clinical-ai-creates-moral-deskilling-through-ethical-judgment-erosion.md`) contain type, domain, confidence, source, created, description, title, agent, sourced_from, scope, sourcer, and relationship fields as required for claims; the four enriched existing claims maintain their proper schema; no entity files are present in this PR. ## 2. Duplicate/redundancy The enrichments add genuinely new evidence from Natali et al. 2025 to existing claims without duplicating content already present—the cross-specialty pattern claim gains synthesis evidence, the three-failure-modes claim gains the fourth mode (moral deskilling), and the never-skilling claims gain the formalized "upskilling inhibition" terminology and mechanistic explanation that wasn't previously documented. ## 3. Confidence Both new claims are marked "experimental" which is appropriate given they introduce novel concepts (upskilling inhibition formalization, moral deskilling) from a single 2025 mixed-method review that hasn't yet been validated by independent replication or longitudinal outcome data. ## 4. Wiki links Multiple wiki links in the `related` and `supports` fields use natural language titles rather than filenames (e.g., "clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling" vs actual filename format), but as instructed, broken links are expected when linked claims exist in other PRs and do not affect the verdict. ## 5. Source quality Natali et al. 2025 from Springer as a mixed-method review synthesizing evidence across specialties is a credible academic source appropriate for these claims about deskilling patterns, upskilling inhibition mechanisms, and moral deskilling concepts in clinical AI contexts. ## 6. Specificity Both new claims are falsifiable: the upskilling inhibition claim could be disproven by showing trainees acquire skills despite AI handling routine cases, and the moral deskilling claim could be disproven by demonstrating that AI acceptance doesn't erode ethical judgment capacity or that clinicians maintain value-conflict recognition despite routine AI use. --- **Verdict:** All claims are factually supported by the cited source, schema is correct for content types, confidence levels are appropriately calibrated to the evidence strength, and the claims make specific falsifiable assertions. The wiki link formatting issues are expected and do not constitute grounds for requesting changes. <!-- VERDICT:LEO:APPROVE -->