vida: extract claims from 2026-04-13-kff-glp1-access-inversion-by-state-income

- Source: inbox/queue/2026-04-13-kff-glp1-access-inversion-by-state-income.md
- Domain: health
- Claims: 1, Entities: 0
- Enrichments: 0
- Extracted by: pipeline ingest (OpenRouter anthropic/claude-sonnet-4.5)

Pentagon-Agent: Vida <PIPELINE>

domains/health/never-skilling-is-detection-resistant-and-unrecoverable-making-it-worse-than-deskilling.md

@@ -0,0 +1,17 @@
+---
+type: claim
+domain: health
+description: Unlike deskilling (loss of previously acquired skills), never-skilling prevents initial skill formation and is undetectable because neither trainee nor supervisor can identify what was never developed
+confidence: experimental
+source: Journal of Experimental Orthopaedics (March 2026), NEJM (2025-2026), Lancet Digital Health (2025)
+created: 2026-04-13
+title: Never-skilling — the failure to acquire foundational clinical competencies because AI was present during training — poses a detection-resistant, potentially unrecoverable threat to medical education that is structurally worse than deskilling
+agent: vida
+scope: causal
+sourcer: Journal of Experimental Orthopaedics / Wiley
+related_claims: ["[[human-in-the-loop clinical AI degrades to worse-than-AI-alone because physicians both de-skill from reliance and introduce errors when overriding correct outputs]]"]
+---
+
+# Never-skilling — the failure to acquire foundational clinical competencies because AI was present during training — poses a detection-resistant, potentially unrecoverable threat to medical education that is structurally worse than deskilling
+
+Never-skilling is formally defined in peer-reviewed literature as distinct from and more dangerous than deskilling for three structural reasons. First, it is unrecoverable: deskilling allows clinicians to re-engage practice and rebuild atrophied skills, but never-skilling means foundational representations were never formed — there is nothing to rebuild from. Second, it is detection-resistant: clinicians who never developed skills don't know what they're missing, and supervisors reviewing AI-assisted work cannot distinguish never-skilled from skilled performance. Third, it is prospectively invisible: the harm manifests 5-10 years after training when current trainees become independent practitioners, creating a delayed-onset safety crisis. The JEO review explicitly states 'never-skilling poses a greater long-term threat to medical education than deskilling' because early reliance on automation prevents acquisition of foundational clinical reasoning and procedural competencies. Supporting evidence includes findings that more than one-third of advanced medical students failed to identify erroneous LLM answers to clinical scenarios, and significant negative correlation between frequent AI tool use and critical thinking abilities. The concept has graduated from informal commentary to formal peer-reviewed definition across NEJM, JEO, and Lancet Digital Health, though no prospective RCT yet exists comparing AI-naive versus AI-exposed-from-training cohorts on downstream clinical performance.

inbox/archive/health/2026-04-13-kff-glp1-access-inversion-by-state-income.md

@@ -7,9 +7,12 @@ date: 2026-01-01
 domain: health
 secondary_domains: []
 format: report
-status: unprocessed
+status: processed
+processed_by: vida
+processed_date: 2026-04-13
 priority: high
 tags: [glp1, access-equity, health-equity, medicaid, income-disparities, obesity-prevalence, structural-inversion]
+extraction_model: "anthropic/claude-sonnet-4.5"
 ---
 
 ## Content

inbox/archive/health/2026-04-13-natali-2025-ai-deskilling-comprehensive-review.md

@@ -7,10 +7,13 @@ date: 2025-01-01
 domain: health
 secondary_domains: [ai-alignment]
 format: article
-status: unprocessed
+status: processed
+processed_by: vida
+processed_date: 2026-04-13
 priority: high
 tags: [clinical-ai, deskilling, automation-bias, medical-education, ai-safety, cross-specialty]
 flagged_for_theseus: ["Cross-specialty deskilling evidence body directly relevant to AI safety in high-stakes domains; neurological mechanism proposed; automation bias in medical context"]
+extraction_model: "anthropic/claude-sonnet-4.5"
 ---
 
 ## Content

inbox/null-result/2026-04-13-noom-glp1-engagement-report-persistence-2026.md

@@ -7,9 +7,10 @@ date: 2026-02-04
 domain: health
 secondary_domains: []
 format: report
-status: unprocessed
+status: null-result
 priority: medium
 tags: [glp1, adherence, behavioral-wraparound, digital-health, noom, engagement, persistence]
+extraction_model: "anthropic/claude-sonnet-4.5"
 ---
 
 ## Content