vida: extract claims from 2026-04-25-glp1-oud-phase2-trial-protocol-ncta06548490-ascpjournal-2025

- Source: inbox/queue/2026-04-25-glp1-oud-phase2-trial-protocol-ncta06548490-ascpjournal-2025.md
- Domain: health
- Claims: 0, Entities: 0
- Enrichments: 1
- Extracted by: pipeline ingest (OpenRouter anthropic/claude-sonnet-4.5)

Pentagon-Agent: Vida <PIPELINE>

domains/health/ai-induced-deskilling-follows-consistent-cross-specialty-pattern-in-medicine.md

@@ -6,7 +6,7 @@ confidence: likely
 source: Natali et al., Artificial Intelligence Review 2025, mixed-method systematic review
 created: 2026-04-13
 agent: vida
-related: ["Automation bias in medical imaging causes clinicians to anchor on AI output rather than conducting independent reads, increasing false-positive rates by up to 12 percent even among experienced readers", "ai-induced-deskilling-follows-consistent-cross-specialty-pattern-in-medicine", "clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling", "ai-assistance-produces-neurologically-grounded-irreversible-deskilling-through-prefrontal-disengagement-hippocampal-reduction-and-dopaminergic-reinforcement", "never-skilling-is-structurally-invisible-because-it-lacks-pre-ai-baseline-requiring-prospective-competency-assessment", "never-skilling-is-detection-resistant-and-unrecoverable-making-it-worse-than-deskilling", "economic forces push humans out of every cognitive loop where output quality is independently verifiable because human-in-the-loop is a cost that competitive markets eliminate", "no-peer-reviewed-evidence-of-durable-physician-upskilling-from-ai-exposure-as-of-mid-2026", "divergence-human-ai-clinical-collaboration-enhance-or-degrade"]
+related: ["Automation bias in medical imaging causes clinicians to anchor on AI output rather than conducting independent reads, increasing false-positive rates by up to 12 percent even among experienced readers", "ai-induced-deskilling-follows-consistent-cross-specialty-pattern-in-medicine", "clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling", "ai-assistance-produces-neurologically-grounded-irreversible-deskilling-through-prefrontal-disengagement-hippocampal-reduction-and-dopaminergic-reinforcement", "never-skilling-is-structurally-invisible-because-it-lacks-pre-ai-baseline-requiring-prospective-competency-assessment", "never-skilling-is-detection-resistant-and-unrecoverable-making-it-worse-than-deskilling", "economic forces push humans out of every cognitive loop where output quality is independently verifiable because human-in-the-loop is a cost that competitive markets eliminate", "no-peer-reviewed-evidence-of-durable-physician-upskilling-from-ai-exposure-as-of-mid-2026", "divergence-human-ai-clinical-collaboration-enhance-or-degrade", "ai-micro-learning-loop-creates-durable-upskilling-through-review-confirm-override-cycle"]
 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]]"]
 reweave_edges: ["{'AI assistance may produce neurologically-grounded, partially irreversible skill degradation through three concurrent mechanisms': 'prefrontal disengagement, hippocampal memory formation reduction, and dopaminergic reinforcement of AI reliance|supports|2026-04-14'}", "Automation bias in medical imaging causes clinicians to anchor on AI output rather than conducting independent reads, increasing false-positive rates by up to 12 percent even among experienced readers|related|2026-04-14", "Dopaminergic reinforcement of AI-assisted success creates motivational entrenchment that makes deskilling a behavioral incentive problem, not just a training design problem|supports|2026-04-14", "{'AI assistance may produce neurologically-grounded, partially irreversible skill degradation through three concurrent mechanisms': 'prefrontal disengagement, hippocampal memory formation reduction, and dopaminergic reinforcement of AI reliance|supports|2026-04-17'}", "{'AI assistance may produce neurologically-grounded, partially irreversible skill degradation through three concurrent mechanisms': 'prefrontal disengagement, hippocampal memory formation reduction, and dopaminergic reinforcement of AI reliance|supports|2026-04-18'}", "AI assistance may produce neurologically-grounded, partially irreversible skill degradation through three concurrent mechanisms: prefrontal disengagement, hippocampal memory formation reduction, and dopaminergic reinforcement of AI reliance|supports|2026-04-19"]
 scope: causal
@@ -46,3 +46,10 @@ Radiology residents using AI assistance showed resilience to large AI errors (>3
 **Source:** Heudel et al., Insights into Imaging, Jan 2025 (PMC11780016)
 
 The Heudel radiology study is frequently cited (including by Oettl 2026) as evidence for AI-induced upskilling, creating apparent contradiction with deskilling evidence. However, close reading reveals it only shows performance improvement with AI present, not durable skill acquisition. The study's own title poses 'Upskilling or Deskilling?' as an open question, and the data cannot answer it without a post-training, no-AI assessment arm. This represents the core methodological limitation in the upskilling literature: conflating AI-assistance effects with learning effects.
+
+
+## Extending Evidence
+
+**Source:** El Tarhouny & Farghaly, Frontiers in Medicine 2026
+
+Deskilling affects the full medical education continuum with distinct risk profiles: medical students face never-skilling (never developing independent reasoning before AI becomes standard), residents face partial-skilling (developing incomplete skills then transitioning to AI environments), and practicing clinicians face sustained deskilling from years of AI reliance. The paper defines deskilling as 'the gradual erosion of independent clinical reasoning skills, together with crucial elements of clinical competence.'

domains/health/clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling.md

@@ -67,3 +67,10 @@ The Heudel study design inadvertently demonstrates why never-skilling is detecti
 **Source:** ARISE Network State of Clinical AI Report 2026
 
 ARISE 2026 report documents zero current deskilling in practicing clinicians but 33% of younger providers rank deskilling as top-2 concern versus 11% of older providers, providing quantitative evidence for the temporal distribution of skill failure modes across career stages
+
+
+## Extending Evidence
+
+**Source:** El Tarhouny & Farghaly, Frontiers in Medicine 2026
+
+The continuum framing shows never-skilling affects trainees who never develop baseline competency before AI adoption, while deskilling affects experienced physicians who lose previously acquired skills. The paper traces this across medical students → residents → practicing clinicians, with each population facing different risk profiles based on their pre-AI skill development stage.

domains/health/glp1-receptor-agonists-address-substance-use-disorders-through-mesolimbic-dopamine-modulation.md

@@ -11,7 +11,7 @@ sourced_from: health/2026-04-23-glp1-substance-use-disorder-33-trials.md
 scope: causal
 sourcer: PubMed/ClinicalTrials.gov systematic review
 challenges: ["medical care explains only 10-20 percent of health outcomes because behavioral social and genetic factors dominate as four independent methodologies confirm"]
-related: ["glp-1-receptor-agonists-require-continuous-treatment-because-metabolic-benefits-reverse-within-28-52-weeks-of-discontinuation", "medical care explains only 10-20 percent of health outcomes because behavioral social and genetic factors dominate as four independent methodologies confirm", "glp1-receptor-agonists-address-substance-use-disorders-through-mesolimbic-dopamine-modulation", "hedonic-eating-dopamine-circuit-adapts-to-glp1-suppression-explaining-continuous-delivery-requirement"]
+related: ["glp-1-receptor-agonists-require-continuous-treatment-because-metabolic-benefits-reverse-within-28-52-weeks-of-discontinuation", "medical care explains only 10-20 percent of health outcomes because behavioral social and genetic factors dominate as four independent methodologies confirm", "glp1-receptor-agonists-address-substance-use-disorders-through-mesolimbic-dopamine-modulation", "hedonic-eating-dopamine-circuit-adapts-to-glp1-suppression-explaining-continuous-delivery-requirement", "behavioral-biological-health-dichotomy-false-for-reward-dysregulation-conditions"]
 supports: ["The behavioral-biological health determinant dichotomy is false for obesity because what appears as behavioral overconsumption is dopamine reward dysregulation continuously activated by the food environment", "Hedonic eating is mediated by dopamine reward circuits that adapt to GLP-1 suppression explaining both why GLP-1s work and why they require continuous delivery"]
 reweave_edges: ["The behavioral-biological health determinant dichotomy is false for obesity because what appears as behavioral overconsumption is dopamine reward dysregulation continuously activated by the food environment|supports|2026-04-24", "Hedonic eating is mediated by dopamine reward circuits that adapt to GLP-1 suppression explaining both why GLP-1s work and why they require continuous delivery|supports|2026-04-24"]
 ---
@@ -53,3 +53,10 @@ Meta-analysis of 14 studies (n=5,262,278) shows pooled AUDIT score reduction of
 **Source:** Qeadan F et al., Addiction 2025
 
 Qeadan et al. (2025) retrospective cohort study of 1.3M patients across 136 US health systems found GLP-1 RA prescriptions associated with 40% lower opioid overdose rates (IRR 0.60, 95% CI 0.43-0.83) in OUD cohort and 50% lower alcohol intoxication rates (IRR 0.50, 95% CI 0.40-0.63) in AUD cohort over 24-month follow-up. Effects consistent across T2DM, obesity, and combined subgroups. This is the largest-scale human data on GLP-1 for opioid outcomes, though observational design creates substantial healthy user bias concerns (patients receiving GLP-1 are more healthcare-engaged, financially able, and motivated). The consistency across subgroups (whether prescribed for diabetes or obesity) reduces some confounding concern. Published in Addiction (Wiley) with formal commentary noting need for prospective RCTs.
+
+
+## Extending Evidence
+
+**Source:** Grigson PS et al., Addiction Science & Clinical Practice 2025
+
+NCT06548490 is the first Phase 2 RCT testing semaglutide for treatment-refractory OUD (n=200, patients already on buprenorphine/methadone who continue illicit use). Trial enrolled first participant January 2025, expected completion November 2026. Protocol formally published in Addiction Science & Clinical Practice (May 2025, PMID 40502777). This represents the definitive human trial that will either confirm or refute the animal/observational signal for OUD, extending the mechanism from AUD to opioid use disorders.

domains/health/moral-deskilling-from-ai-erodes-ethical-judgment-through-repeated-cognitive-offloading.md

@@ -0,0 +1,19 @@
+---
+type: claim
+domain: health
+description: AI reliance degrades physicians' ethical sensitivity and moral reasoning capacity through neural adaptation, not addressed by standard human-in-the-loop safeguards
+confidence: experimental
+source: "El Tarhouny & Farghaly, Frontiers in Medicine 2026"
+created: 2026-04-25
+title: Moral deskilling from AI erodes ethical judgment through repeated cognitive offloading creating a safety risk distinct from diagnostic accuracy
+agent: vida
+sourced_from: health/2026-04-25-frontiers-2026-deskilling-dilemma-brain-over-automation.md
+scope: causal
+sourcer: El Tarhouny S, Farghaly A
+supports: ["ai-assistance-produces-neurologically-grounded-irreversible-deskilling-through-prefrontal-disengagement-hippocampal-reduction-and-dopaminergic-reinforcement"]
+related: ["human-in-the-loop-clinical-ai-degrades-to-worse-than-ai-alone", "clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling", "ai-induced-deskilling-follows-consistent-cross-specialty-pattern-in-medicine", "ai-assistance-produces-neurologically-grounded-irreversible-deskilling-through-prefrontal-disengagement-hippocampal-reduction-and-dopaminergic-reinforcement", "ai-micro-learning-loop-creates-durable-upskilling-through-review-confirm-override-cycle"]
+---
+
+# Moral deskilling from AI erodes ethical judgment through repeated cognitive offloading creating a safety risk distinct from diagnostic accuracy
+
+The paper introduces 'moral deskilling' as a distinct category of AI-induced harm separate from diagnostic deskilling. While diagnostic deskilling affects clinical accuracy (forming differential diagnoses, physical examination skills), moral deskilling affects ethical judgment capacity. The mechanism is neural adaptation from repeated cognitive offloading: 'when individuals repeatedly offload cognitive tasks to external support, neural adaptation occurs in ways that reduce independent learning and reasoning capacity.' This creates a safety failure mode where physicians physically review AI outputs but with diminished ethical reasoning capacity to recognize when AI suggestions conflict with patients' best interests or values. Standard 'physician remains in the loop' safeguards assume the physician retains full ethical judgment capacity, but moral deskilling undermines this assumption. The paper argues this affects the full medical education continuum: medical students may never develop ethical sensitivity before AI becomes standard (never-skilling), residents develop partial capacity then transition to AI environments, and practicing clinicians experience sustained erosion over years. The risk is qualitatively different from missing a diagnosis—it's systematic ethical judgment failure that may be invisible and affect patient care across all interactions.

inbox/archive/health/2026-04-25-frontiers-2026-deskilling-dilemma-brain-over-automation.md

@@ -7,9 +7,12 @@ date: 2026-01-01
 domain: health
 secondary_domains: [ai-alignment]
 format: review
-status: unprocessed
+status: processed
+processed_by: vida
+processed_date: 2026-04-25
 priority: medium
 tags: [clinical-ai, deskilling, moral-deskilling, diagnostic-deskilling, automation, medical-education, clinical-reasoning]
+extraction_model: "anthropic/claude-sonnet-4.5"
 ---
 
 ## Content

inbox/archive/health/2026-04-25-glp1-oud-phase2-trial-protocol-ncta06548490-ascpjournal-2025.md

@@ -7,9 +7,12 @@ date: 2025-05-01
 domain: health
 secondary_domains: []
 format: trial-protocol
-status: unprocessed
+status: processed
+processed_by: vida
+processed_date: 2026-04-25
 priority: low
 tags: [GLP-1, semaglutide, OUD, opioid-use-disorder, clinical-trial, addiction, reward-circuit, VTA-dopamine]
+extraction_model: "anthropic/claude-sonnet-4.5"
 ---
 
 ## Content