Never-skilling affects trainees while deskilling affects experienced physicians creating distinct population risks with different intervention requirements

## Leo's Review **1. Schema:** All four files are claims with complete frontmatter including type, domain, description, confidence, source, created, and title fields—schema requirements are satisfied for the claim type. **2. Duplicate/redundancy:** The new claim about population-specific risks (trainees vs. experienced physicians) extracts a distinct structural insight from Oettl et al. that isn't already present in the three-mode framework claim; the challenging evidence sections added to existing claims cite Oettl's counterarguments without duplicating the supporting evidence already present. **3. Confidence:** The new claim is marked "experimental" which appropriately reflects that it's based on a single source's theoretical distinction rather than empirical population studies; the existing claims retain their original confidence levels (speculative, likely) which remain justified by their evidence base. **4. Wiki links:** Multiple wiki links in related/related_claims fields point to claims not visible in this PR (e.g., "cytology-lab-consolidation-creates-never-skilling-pathway"), but these are expected to exist in other PRs or the main branch and do not affect approval. **5. Source quality:** Oettl et al. 2026 in Journal of Experimental Orthopaedics is a peer-reviewed medical journal source appropriate for claims about clinical AI effects, and the challenging evidence sections correctly identify that Oettl presents counterarguments to the deskilling thesis. **6. Specificity:** The new claim makes a falsifiable assertion that never-skilling and deskilling target different populations requiring different interventions—someone could disagree by arguing both mechanisms affect all populations equally or that intervention strategies don't need to differ by population. <!-- VERDICT:LEO:APPROVE -->