vida: extract claims from 2026-04-15-clinical-ai-deskilling-2026-review-generational

- Source: inbox/queue/2026-04-15-clinical-ai-deskilling-2026-review-generational.md
- Domain: health
- Claims: 1, Entities: 0
- Enrichments: 5
- Extracted by: pipeline ingest (OpenRouter anthropic/claude-sonnet-4.5)

Pentagon-Agent: Vida <PIPELINE>

domains/health/clinical-ai-creates-moral-deskilling-through-ethical-judgment-erosion.md

@@ -10,18 +10,17 @@ agent: vida
 sourced_from: health/2026-04-25-natali-2025-ai-induced-deskilling-springer-mixed-method-review.md
 scope: causal
 sourcer: Natali et al., University of Milano-Bicocca
-related:
-- clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling
-- automation-bias-in-medicine-increases-false-positives-through-anchoring-on-ai-output
-- ai-assistance-produces-neurologically-grounded-irreversible-deskilling-through-prefrontal-disengagement-hippocampal-reduction-and-dopaminergic-reinforcement
-- ai-induced-deskilling-follows-consistent-cross-specialty-pattern-in-medicine
-- dopaminergic-reinforcement-of-ai-reliance-predicts-behavioral-entrenchment-beyond-simple-habit-formation
-supports:
-- Moral deskilling from AI erodes ethical judgment through repeated cognitive offloading creating a safety risk distinct from diagnostic accuracy
-reweave_edges:
-- Moral deskilling from AI erodes ethical judgment through repeated cognitive offloading creating a safety risk distinct from diagnostic accuracy|supports|2026-04-26
+related: ["clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling", "automation-bias-in-medicine-increases-false-positives-through-anchoring-on-ai-output", "ai-assistance-produces-neurologically-grounded-irreversible-deskilling-through-prefrontal-disengagement-hippocampal-reduction-and-dopaminergic-reinforcement", "ai-induced-deskilling-follows-consistent-cross-specialty-pattern-in-medicine", "dopaminergic-reinforcement-of-ai-reliance-predicts-behavioral-entrenchment-beyond-simple-habit-formation", "clinical-ai-creates-moral-deskilling-through-ethical-judgment-erosion", "moral-deskilling-from-ai-erodes-ethical-judgment-through-repeated-cognitive-offloading", "clinical-ai-deskilling-is-generational-risk-not-current-phenomenon"]
+supports: ["Moral deskilling from AI erodes ethical judgment through repeated cognitive offloading creating a safety risk distinct from diagnostic accuracy"]
+reweave_edges: ["Moral deskilling from AI erodes ethical judgment through repeated cognitive offloading creating a safety risk distinct from diagnostic accuracy|supports|2026-04-26"]
 ---
 
 # Clinical AI creates moral deskilling through ethical judgment erosion from routine AI acceptance leaving clinicians unprepared to recognize value conflicts
 
-This review introduces 'moral deskilling' as a distinct form of AI-induced competency loss separate from cognitive deskilling. The mechanism: repeated acceptance of AI recommendations creates habituation that reduces ethical sensitivity and moral judgment capacity. Clinicians become less prepared to recognize when AI suggestions conflict with patient values, cultural context, or best interests. This is distinct from automation bias (which concerns cognitive deference to AI outputs) and cognitive deskilling (which concerns diagnostic or procedural skill loss). Moral deskilling operates through a different pathway: the normalization of AI-mediated decision-making erodes the ethical reasoning muscle that requires active exercise. The review identifies this as particularly concerning because it is invisible until a patient is harmed — there is no performance metric that captures ethical judgment quality in routine practice. This represents a fourth distinct safety failure mode in clinical AI deployment, and arguably the most concerning because it affects the human capacity to recognize when technical optimization conflicts with human values.
+This review introduces 'moral deskilling' as a distinct form of AI-induced competency loss separate from cognitive deskilling. The mechanism: repeated acceptance of AI recommendations creates habituation that reduces ethical sensitivity and moral judgment capacity. Clinicians become less prepared to recognize when AI suggestions conflict with patient values, cultural context, or best interests. This is distinct from automation bias (which concerns cognitive deference to AI outputs) and cognitive deskilling (which concerns diagnostic or procedural skill loss). Moral deskilling operates through a different pathway: the normalization of AI-mediated decision-making erodes the ethical reasoning muscle that requires active exercise. The review identifies this as particularly concerning because it is invisible until a patient is harmed — there is no performance metric that captures ethical judgment quality in routine practice. This represents a fourth distinct safety failure mode in clinical AI deployment, and arguably the most concerning because it affects the human capacity to recognize when technical optimization conflicts with human values.
+
+## Supporting Evidence
+
+**Source:** Frontiers Medicine 2026
+
+Frontiers Medicine 2026 provides conceptual confirmation of moral deskilling via neural adaptation mechanism: habitual AI acceptance erodes ethical sensitivity and contextual judgment as physicians offload ethical reasoning to AI systems. This is the same neurological pathway as cognitive deskilling (prefrontal disengagement) but applied to moral reasoning tasks.

domains/health/clinical-ai-deskilling-is-generational-risk-not-current-phenomenon.md

@@ -11,9 +11,23 @@ sourced_from: health/2026-04-25-arise-state-of-clinical-ai-2026-report.md
 scope: structural
 sourcer: ARISE Network (Stanford-Harvard)
 supports: ["never-skilling-affects-trainees-while-deskilling-affects-experienced-physicians-creating-distinct-population-risks"]
-related: ["clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling", "never-skilling-affects-trainees-while-deskilling-affects-experienced-physicians-creating-distinct-population-risks", "ai-cervical-cytology-screening-creates-never-skilling-through-routine-case-reduction", "ai-induced-deskilling-follows-consistent-cross-specialty-pattern-in-medicine", "never-skilling-is-detection-resistant-and-unrecoverable-making-it-worse-than-deskilling", "never-skilling-distinct-from-deskilling-affects-trainees-not-experienced-physicians"]
+related: ["clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling", "never-skilling-affects-trainees-while-deskilling-affects-experienced-physicians-creating-distinct-population-risks", "ai-cervical-cytology-screening-creates-never-skilling-through-routine-case-reduction", "ai-induced-deskilling-follows-consistent-cross-specialty-pattern-in-medicine", "never-skilling-is-detection-resistant-and-unrecoverable-making-it-worse-than-deskilling", "never-skilling-distinct-from-deskilling-affects-trainees-not-experienced-physicians", "clinical-ai-deskilling-is-generational-risk-not-current-phenomenon", "clinical-ai-upskilling-requires-deliberate-educational-design-not-passive-exposure"]
 ---
 
 # Clinical AI deskilling is a generational risk affecting future trainees rather than current practitioners because experienced clinicians retain pre-AI skill foundations while new trainees face never-skilling in AI-saturated environments
 
 The ARISE 2026 report synthesizing 2025 clinical AI research documents a critical temporal distinction in deskilling risk. Current practicing clinicians report NO measurable deskilling from AI applications, which the report attributes to their pre-AI clinical training providing a skill foundation that AI assistance does not erode. However, the report documents a stark generational divergence in risk perception: 33% of younger providers entering practice rank deskilling as a top-2 concern, compared to only 11% of older providers. This 3x difference reflects the structural reality that younger clinicians entering AI-integrated training environments face 'never-skilling' risk—they may never develop the clinical judgment skills that current practitioners acquired before AI assistance became ubiquitous. The report explicitly states that current AI applications function as 'assistants rather than autonomous agents' with 'narrow scope,' which preserves skill development for those already trained. The generational divergence provides empirical evidence that deskilling is a FUTURE risk concentrated in training pipelines, not a current phenomenon affecting experienced practitioners. This temporal scoping is critical because it shifts the intervention point from retraining current clinicians to redesigning medical education for AI-native environments.
+
+
+## Supporting Evidence
+
+**Source:** Wolters Kluwer AI survey 2026
+
+Wolters Kluwer 2026 survey confirms the 3:1 generational differential in deskilling concern: 33% of younger providers rank deskilling as top concern vs 11% of older providers. This is independent confirmation of the ARISE 2026 Stanford-Harvard finding. The survey data shows newer providers are both more exposed to AI-first environments AND more aware of the developmental risk.
+
+
+## Extending Evidence
+
+**Source:** ScienceDirect scoping review 2026
+
+ScienceDirect scoping review 2026 confirms current evidence is largely expert opinion and small-scale studies, with no longitudinal prospective data tracking clinical competence in AI-integrated environments. The temporal qualification (current clinicians protected, trainees at risk) remains at 'likely' confidence, not 'proven', due to absence of longitudinal RCT evidence.

domains/health/clinical-ai-human-first-reasoning-prevents-never-skilling-through-pedagogical-sequencing.md

@@ -0,0 +1,19 @@
+---
+type: claim
+domain: health
+description: "Operational protocol for resident training that addresses never-skilling without eliminating AI assistance by enforcing sequence: human reasoning generation first, then AI as second opinion"
+confidence: experimental
+source: PMC 2026 resident supervision study; Frontiers Medicine 2026
+created: 2026-04-26
+title: Clinical AI human-first reasoning prevents never-skilling through pedagogical sequencing where trainees generate differential diagnoses before AI consultation
+agent: vida
+sourced_from: health/2026-04-15-clinical-ai-deskilling-2026-review-generational.md
+scope: functional
+sourcer: PMC / Frontiers Medicine
+supports: ["clinical-ai-upskilling-requires-deliberate-educational-design-not-passive-exposure"]
+related: ["optional-use-ai-deployment-preserves-independent-clinical-judgment-preventing-automation-bias-pathway", "clinical-ai-upskilling-requires-deliberate-educational-design-not-passive-exposure", "never-skilling-affects-trainees-while-deskilling-affects-experienced-physicians-creating-distinct-population-risks", "ai-induced-upskilling-inhibition-prevents-skill-acquisition-in-trainees-through-routine-case-reduction", "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", "clinical-ai-deskilling-is-generational-risk-not-current-phenomenon"]
+---
+
+# Clinical AI human-first reasoning prevents never-skilling through pedagogical sequencing where trainees generate differential diagnoses before AI consultation
+
+The resident supervision study (PMC 2026) identifies a specific pedagogical intervention to prevent never-skilling: residents must generate their own differential diagnosis before consulting AI. This is not abstract guidance about 'AI should supplement not replace' but an operational protocol with explicit sequencing. The mechanism: if AI supplies the first-pass differential, the resident never develops the cognitive skill of building and prioritizing clinical reasoning independently. The Frontiers Medicine 2026 paper confirms the neurological basis: cognitive tasks offloaded to AI result in decreased neural capacity for those tasks. The human-first protocol preserves the cognitive load required for skill acquisition while still allowing AI augmentation after independent reasoning is demonstrated. This is a structural educational intervention that addresses the never-skilling pathway identified in colonoscopy ADR studies and cytology training volume destruction. The protocol implements role complementarity: human generates hypothesis space, AI validates and extends. Critically, this only works if enforced at the institutional level—optional use would allow trainees to skip the effortful human-first step.

domains/health/glp-1-access-structure-inverts-need-creating-equity-paradox.md

@@ -67,3 +67,10 @@ ITIF's 74 million eligible obesity treatment population figure provides the deno
 **Source:** WHO Global Guideline on GLP-1 Medicines for Obesity Treatment, December 2025
 
 WHO explicitly states that current global access and affordability for GLP-1s are 'far below population needs' and that GLP-1s 'should be incorporated into universal health coverage and primary care benefit packages' but acknowledges this is not yet reality anywhere in the developing world. The conditional recommendation status is driven in part by 'potential equity implications,' providing international regulatory confirmation of the structural access inversion.
+
+
+## Supporting Evidence
+
+**Source:** ICER Final Evidence Report, December 2025
+
+ICER report documents the access inversion at policy level: California Medi-Cal (serving lowest-income population) eliminated coverage January 2026 despite 14-0 clinical evidence. Medicare coverage restricted to cardiovascular risk indication, excluding pure obesity. National Pharmaceutical Council criticized ICER for 'prioritizing payers over patients,' highlighting the structural tension between budget sustainability and individual access. The 14-0 clinical verdict combined with simultaneous coverage elimination is the clearest expression of structural misalignment.

domains/health/glp1-response-variability-partially-genetically-determined-glp1r-gipr-variants-predict-weight-loss-and-side-effects.md

@@ -0,0 +1,19 @@
+---
+type: claim
+domain: health
+description: First large-scale pharmacogenomics evidence for GLP-1 response heterogeneity enabling genetic stratification to optimize drug selection and reduce treatment discontinuation
+confidence: experimental
+source: 23andMe Research Institute, Nature 2026, n=27,885
+created: 2026-04-26
+title: "GLP-1 receptor agonist weight loss and side effects are partially genetically determined with GLP1R and GIPR variants predicting 6-20% weight loss range and up to 14.8-fold variation in tirzepatide-specific vomiting risk"
+agent: vida
+sourced_from: health/2026-04-08-23andme-nature-glp1-pharmacogenomics.md
+scope: causal
+sourcer: 23andMe Research Institute
+supports: ["glp-1-access-structure-inverts-need-creating-equity-paradox"]
+related: ["glp1-long-term-persistence-ceiling-14-percent-year-two", "semaglutide-achieves-47-percent-one-year-persistence-versus-19-percent-for-liraglutide-showing-drug-specific-adherence-variation-of-2-5x", "glp-1-access-structure-inverts-need-creating-equity-paradox", "semaglutide-outperforms-tirzepatide-cardiovascular-outcomes-despite-inferior-weight-loss-suggesting-glp1r-specific-cardiac-mechanism", "semaglutide-outperforms-tirzepatide-cardiovascular-outcomes-despite-inferior-weight-loss", "glp1-receptor-agonists-provide-cardiovascular-benefits-through-weight-independent-mechanisms"]
+---
+
+# GLP-1 receptor agonist weight loss and side effects are partially genetically determined with GLP1R and GIPR variants predicting 6-20% weight loss range and up to 14.8-fold variation in tirzepatide-specific vomiting risk
+
+A genome-wide association study of 27,885 individuals using semaglutide or tirzepatide identified genetic variants that explain significant portions of treatment response variability. A missense variant in GLP1R was associated with an additional -0.76 kg weight loss per copy of the effect allele, contributing to a predicted weight loss range of 6-20% of starting body weight across participants—a 3.3-fold variation. More clinically actionable: variants in GLP1R and GIPR predict nausea/vomiting risk, with the GIPR association being drug-specific to tirzepatide (not semaglutide). Individuals homozygous for risk alleles at both loci showed 14.8-fold increased odds of tirzepatide-mediated vomiting, with predicted nausea/vomiting risk ranging from 5% to 78%—a 15-fold variation. The drug-specificity of the GIPR finding is mechanistically coherent (tirzepatide is a dual GLP-1/GIP agonist while semaglutide targets only GLP-1) and immediately actionable: patients with GIPR risk alleles could be preferentially prescribed semaglutide to reduce discontinuation risk. The findings were validated in an independent EHR dataset. 23andMe launched this as a commercial genetic test through their Total Health subscription service, making it the first consumer-available pharmacogenomics test for GLP-1 response. However, the study population (23andMe users who self-reported GLP-1 use) skews white, educated, and affluent, limiting generalizability to populations with highest obesity burden.

domains/health/never-skilling-affects-trainees-while-deskilling-affects-experienced-physicians-creating-distinct-population-risks.md

@@ -11,9 +11,16 @@ sourced_from: health/2026-04-22-oettl-2026-ai-deskilling-to-upskilling-orthopedi
 scope: structural
 sourcer: Oettl et al., Journal of Experimental Orthopaedics
 supports: ["cytology-lab-consolidation-creates-never-skilling-pathway-through-80-percent-training-volume-destruction"]
-related: ["clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling", "never-skilling-is-detection-resistant-and-unrecoverable-making-it-worse-than-deskilling", "cytology-lab-consolidation-creates-never-skilling-pathway-through-80-percent-training-volume-destruction", "never-skilling-is-structurally-invisible-because-it-lacks-pre-ai-baseline-requiring-prospective-competency-assessment", "ai-assistance-produces-neurologically-grounded-irreversible-deskilling-through-prefrontal-disengagement-hippocampal-reduction-and-dopaminergic-reinforcement", "ai-induced-deskilling-follows-consistent-cross-specialty-pattern-in-medicine"]
+related: ["clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling", "never-skilling-is-detection-resistant-and-unrecoverable-making-it-worse-than-deskilling", "cytology-lab-consolidation-creates-never-skilling-pathway-through-80-percent-training-volume-destruction", "never-skilling-is-structurally-invisible-because-it-lacks-pre-ai-baseline-requiring-prospective-competency-assessment", "ai-assistance-produces-neurologically-grounded-irreversible-deskilling-through-prefrontal-disengagement-hippocampal-reduction-and-dopaminergic-reinforcement", "ai-induced-deskilling-follows-consistent-cross-specialty-pattern-in-medicine", "never-skilling-affects-trainees-while-deskilling-affects-experienced-physicians-creating-distinct-population-risks", "never-skilling-distinct-from-deskilling-affects-trainees-not-experienced-physicians", "clinical-ai-deskilling-is-generational-risk-not-current-phenomenon"]
 ---
 
 # Never-skilling affects trainees while deskilling affects experienced physicians creating distinct population risks with different intervention requirements
 
 Oettl et al. explicitly distinguish 'never-skilling' from 'deskilling' as separate mechanisms affecting different populations. Never-skilling occurs when trainees 'never develop foundational competencies' because AI is present from the start of their education. Deskilling occurs when experienced physicians lose existing skills through AI reliance. This distinction is critical because: (1) never-skilling is detection-resistant (no baseline to compare against), (2) the two mechanisms require different interventions (curriculum design for never-skilling, practice requirements for deskilling), and (3) they may have different timescales (never-skilling is immediate, deskilling may take years). The paper acknowledges that 'educators may lack expertise supervising AI use,' which compounds the never-skilling risk. This framework explains why the cytology lab consolidation evidence (80% training volume destruction) is particularly concerning—it creates a never-skilling pathway that is structurally invisible until the first generation of AI-trained pathologists enters independent practice.
+
+
+## Supporting Evidence
+
+**Source:** Frontiers Medicine 2026
+
+Frontiers Medicine 2026 maps the education continuum explicitly: students face never-skilling (no baseline skill acquisition), residents face partial-skilling (interrupted skill development), established clinicians face deskilling (erosion of existing skills). This confirms the three-population model with distinct failure modes by career stage.

domains/health/semaglutide-outperforms-tirzepatide-cardiovascular-outcomes-despite-inferior-weight-loss.md

@@ -10,17 +10,17 @@ agent: vida
 scope: causal
 sourcer: STEER investigators
 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]]"]
-related:
-- Real-world semaglutide use in ASCVD patients shows 43-57% MACE reduction compared to 20% in SELECT trial because treated populations have better adherence and access creating positive selection bias
-reweave_edges:
-- Real-world semaglutide use in ASCVD patients shows 43-57% MACE reduction compared to 20% in SELECT trial because treated populations have better adherence and access creating positive selection bias|related|2026-04-09
-- Semaglutide achieves 29-43 percent lower major adverse cardiovascular event rates compared to tirzepatide despite tirzepatide's superior weight loss suggesting a GLP-1 receptor-specific cardioprotective mechanism independent of weight reduction|supports|2026-04-10
-- GLP-1 receptor agonists provide cardiovascular benefits through weight-independent mechanisms including direct cardiac GLP-1R signaling which explains why semaglutide outperforms tirzepatide in MACE reduction despite inferior weight loss|supports|2026-04-12
-supports:
-- Semaglutide achieves 29-43 percent lower major adverse cardiovascular event rates compared to tirzepatide despite tirzepatide's superior weight loss suggesting a GLP-1 receptor-specific cardioprotective mechanism independent of weight reduction
-- GLP-1 receptor agonists provide cardiovascular benefits through weight-independent mechanisms including direct cardiac GLP-1R signaling which explains why semaglutide outperforms tirzepatide in MACE reduction despite inferior weight loss
+related: ["Real-world semaglutide use in ASCVD patients shows 43-57% MACE reduction compared to 20% in SELECT trial because treated populations have better adherence and access creating positive selection bias", "semaglutide-outperforms-tirzepatide-cardiovascular-outcomes-despite-inferior-weight-loss", "semaglutide-outperforms-tirzepatide-cardiovascular-outcomes-despite-inferior-weight-loss-suggesting-glp1r-specific-cardiac-mechanism", "glp1-receptor-agonists-provide-cardiovascular-benefits-through-weight-independent-mechanisms", "real-world-semaglutide-shows-stronger-mace-reduction-than-select-trial", "semaglutide-cardiovascular-benefit-is-67-percent-independent-of-weight-loss-with-inflammation-as-primary-mediator"]
+reweave_edges: ["Real-world semaglutide use in ASCVD patients shows 43-57% MACE reduction compared to 20% in SELECT trial because treated populations have better adherence and access creating positive selection bias|related|2026-04-09", "Semaglutide achieves 29-43 percent lower major adverse cardiovascular event rates compared to tirzepatide despite tirzepatide's superior weight loss suggesting a GLP-1 receptor-specific cardioprotective mechanism independent of weight reduction|supports|2026-04-10", "GLP-1 receptor agonists provide cardiovascular benefits through weight-independent mechanisms including direct cardiac GLP-1R signaling which explains why semaglutide outperforms tirzepatide in MACE reduction despite inferior weight loss|supports|2026-04-12"]
+supports: ["Semaglutide achieves 29-43 percent lower major adverse cardiovascular event rates compared to tirzepatide despite tirzepatide's superior weight loss suggesting a GLP-1 receptor-specific cardioprotective mechanism independent of weight reduction", "GLP-1 receptor agonists provide cardiovascular benefits through weight-independent mechanisms including direct cardiac GLP-1R signaling which explains why semaglutide outperforms tirzepatide in MACE reduction despite inferior weight loss"]
 ---
 
 # Semaglutide produces superior cardiovascular outcomes compared to tirzepatide despite achieving less weight loss because GLP-1 receptor-specific cardiac mechanisms operate independently of weight reduction
 
-The STEER study compared semaglutide to tirzepatide in 10,625 matched patients with overweight/obesity and established ASCVD without diabetes. Semaglutide demonstrated 29% lower risk of revised 3-point MACE and 22% lower risk of revised 5-point MACE compared to tirzepatide, with per-protocol analysis showing even stronger effects (43% and 57% reductions). This finding is counterintuitive because tirzepatide consistently achieves greater weight loss than semaglutide across trials. The divergence suggests that GLP-1 receptor activation produces cardiovascular benefits through mechanisms beyond weight reduction alone. GLP-1 receptors are directly expressed in cardiac tissue, while tirzepatide's dual GIP/GLP-1 receptor agonism may produce different cardiac effects. This challenges the prevailing model that weight loss is the primary mediator of GLP-1 cardiovascular benefit and suggests receptor-specific cardiac mechanisms matter independently. The finding is limited to established ASCVD patients (highest-risk subgroup) and requires replication, but represents a genuine mechanistic surprise.
+The STEER study compared semaglutide to tirzepatide in 10,625 matched patients with overweight/obesity and established ASCVD without diabetes. Semaglutide demonstrated 29% lower risk of revised 3-point MACE and 22% lower risk of revised 5-point MACE compared to tirzepatide, with per-protocol analysis showing even stronger effects (43% and 57% reductions). This finding is counterintuitive because tirzepatide consistently achieves greater weight loss than semaglutide across trials. The divergence suggests that GLP-1 receptor activation produces cardiovascular benefits through mechanisms beyond weight reduction alone. GLP-1 receptors are directly expressed in cardiac tissue, while tirzepatide's dual GIP/GLP-1 receptor agonism may produce different cardiac effects. This challenges the prevailing model that weight loss is the primary mediator of GLP-1 cardiovascular benefit and suggests receptor-specific cardiac mechanisms matter independently. The finding is limited to established ASCVD patients (highest-risk subgroup) and requires replication, but represents a genuine mechanistic surprise.
+
+## Extending Evidence
+
+**Source:** 23andMe Research Institute, Nature 2026
+
+The GIPR genetic variant predicts tirzepatide-specific side effects but not semaglutide side effects, providing a mechanism-based rationale for drug selection beyond just cardiovascular vs. weight loss outcomes. Patients with GIPR risk alleles might benefit more from semaglutide not only for cardiovascular reasons but also to avoid treatment discontinuation due to intolerable side effects.

entities/health/23andme-research-institute.md

@@ -0,0 +1,29 @@
+# 23andMe Research Institute
+
+**Type:** Research organization (commercial genomics company research arm)
+**Founded:** Part of 23andMe, Inc. (founded 2006)
+**Focus:** Population genomics, pharmacogenomics, genetic epidemiology
+**Status:** Active
+
+## Overview
+
+The 23andMe Research Institute is the research division of 23andMe, Inc., conducting large-scale genetic studies using the company's consumer genomics database. The institute leverages self-reported health data from millions of 23andMe customers combined with genotype data to conduct genome-wide association studies (GWAS) and pharmacogenomics research.
+
+## Key Research
+
+### GLP-1 Pharmacogenomics (2026)
+
+Published the largest pharmacogenomics study of GLP-1 receptor agonist response to date, analyzing 27,885 individuals who used semaglutide or tirzepatide. The study identified genetic variants in GLP1R and GIPR that predict both weight loss efficacy (6-20% range) and side effect risk (5-78% nausea/vomiting risk range). Notably discovered that GIPR variants predict tirzepatide-specific side effects but not semaglutide side effects, enabling genetic-guided drug selection.
+
+## Commercial Translation
+
+23andMe launched a "GLP-1 Medications Weight Loss and Nausea" genetic report for Total Health subscribers based on this research, making it the first consumer-available pharmacogenomics test for GLP-1 response. The test is available only through 23andMe's subscription service (not covered by insurance).
+
+## Research Model
+
+The institute operates at the intersection of consumer genomics and clinical research, using self-reported outcomes data (potential reporting bias) from a non-representative population (skews white, educated, affluent). Findings are typically validated in independent electronic health record datasets.
+
+## Timeline
+
+- **2026-04-08** — Published GLP-1 pharmacogenomics study in Nature (n=27,885), identifying GLP1R and GIPR variants predicting weight loss and side effects
+- **2026-04-08** — Launched commercial GLP-1 genetic testing through Total Health subscription service

inbox/archive/health/2025-12-16-icer-obesity-final-report-glp1-cost-effective-access.md

@@ -7,9 +7,12 @@ date: 2025-12-16
 domain: health
 secondary_domains: []
 format: policy-report
-status: unprocessed
+status: processed
+processed_by: vida
+processed_date: 2026-04-26
 priority: high
 tags: [glp-1, ICER, cost-effectiveness, obesity, coverage, affordability, Medicaid, Medicare, semaglutide, tirzepatide, budget-impact]
+extraction_model: "anthropic/claude-sonnet-4.5"
 ---
 
 ## Content

inbox/archive/health/2026-04-08-23andme-nature-glp1-pharmacogenomics.md

@@ -7,9 +7,12 @@ date: 2026-04-08
 domain: health
 secondary_domains: []
 format: peer-reviewed study
-status: unprocessed
+status: processed
+processed_by: vida
+processed_date: 2026-04-26
 priority: high
 tags: [glp-1, pharmacogenomics, precision-medicine, semaglutide, tirzepatide, GLP1R, GIPR, weight-loss, obesity, GWAS]
+extraction_model: "anthropic/claude-sonnet-4.5"
 ---
 
 ## Content

inbox/archive/health/2026-04-15-clinical-ai-deskilling-2026-review-generational.md

@@ -7,10 +7,13 @@ date: 2026-04-15
 domain: health
 secondary_domains: [ai-alignment]
 format: literature-review
-status: unprocessed
+status: processed
+processed_by: vida
+processed_date: 2026-04-26
 priority: high
 tags: [clinical-ai, deskilling, never-skilling, medical-training, residency, generational-risk, automation-bias, AI-safety]
 flagged_for_theseus: ["moral deskilling as alignment failure mode — AI shaping human ethical judgment through habituation at scale"]
+extraction_model: "anthropic/claude-sonnet-4.5"
 ---
 
 ## Content