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/glp1-access-follows-systematic-inversion-highest-burden-states-have-lowest-coverage-and-highest-income-relative-cost.md

@@ -0,0 +1,17 @@
+---
+type: claim
+domain: health
+description: The healthcare system systematically denies access to the populations with the highest disease burden through the combination of state Medicaid policy and income distribution
+confidence: likely
+source: KFF + Health Management Academy, 2025-2026 Medicaid coverage and spending analysis
+created: 2026-04-13
+title: GLP-1 access follows systematic inversion where states with highest obesity prevalence have both lowest Medicaid coverage rates and highest income-relative out-of-pocket costs
+agent: vida
+scope: structural
+sourcer: KFF + Health Management Academy
+related_claims: ["[[GLP-1 receptor agonists are the largest therapeutic category launch in pharmaceutical history but their chronic use model makes the net cost impact inflationary through 2035]]", "[[medical care explains only 10-20 percent of health outcomes because behavioral social and genetic factors dominate as four independent methodologies confirm]]"]
+---
+
+# GLP-1 access follows systematic inversion where states with highest obesity prevalence have both lowest Medicaid coverage rates and highest income-relative out-of-pocket costs
+
+States with the highest obesity rates (Mississippi, West Virginia, Louisiana at 40%+ prevalence) face a triple barrier: (1) only 13 state Medicaid programs cover GLP-1s for obesity as of January 2026 (down from 16 in 2025), and high-burden states are least likely to be among them; (2) these states have the lowest per-capita income; (3) the combination creates income-relative costs of 12-13% of median annual income to maintain continuous GLP-1 treatment in Mississippi/West Virginia/Louisiana tier versus below 8% in Massachusetts/Connecticut tier. Meanwhile, commercial insurance (43% of plans include weight-loss coverage) concentrates in higher-income populations, creating 8x higher GLP-1 utilization in commercial versus Medicaid on a cost-per-prescription basis. This is not an access gap (implying a pathway to close it) but an access inversion—the infrastructure systematically works against the populations who would benefit most. Survey data confirms the structural reality: 70% of Americans believe GLP-1s are accessible only to wealthy people, and only 15% think they're available to anyone who needs them. The majority could afford $100/month or less while standard maintenance pricing is ~$350/month even with manufacturer discounts.

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