auto-fix: strip 30 broken wiki links
Some checks are pending
Mirror PR to Forgejo / mirror (pull_request) Waiting to run
Some checks are pending
Mirror PR to Forgejo / mirror (pull_request) Waiting to run
Pipeline auto-fixer: removed [[ ]] brackets from links that don't resolve to existing claims in the knowledge base.
This commit is contained in:
parent
ad898935f8
commit
fc026bd121
10 changed files with 30 additions and 30 deletions
|
|
@ -119,7 +119,7 @@ The CDC 2024 data provides the most direct evidence to date:
|
|||
**Assessment:** This is a genuinely novel finding with significant implications:
|
||||
1. **Extends GLP-1 therapeutic scope** beyond metabolic disease into behavioral health — a cross-domain connection Vida needs to track
|
||||
2. **Potential new claim candidate:** "GLP-1 receptor agonists demonstrate superior efficacy to approved AUD medications in RCT but carry potential psychiatric risk requiring careful patient selection"
|
||||
3. **KB connection:** Connects to [[the mental health supply gap is widening not closing]] — if GLP-1 can treat AUD pharmacologically, it's a new tool that bypasses the workforce constraint
|
||||
3. **KB connection:** Connects to the mental health supply gap is widening not closing — if GLP-1 can treat AUD pharmacologically, it's a new tool that bypasses the workforce constraint
|
||||
4. **Complication for Clay cross-domain:** Narrative health infrastructure matters for addiction recovery; GLP-1 reduces craving mechanistically but doesn't address the social/narrative dimensions
|
||||
|
||||
---
|
||||
|
|
|
|||
|
|
@ -52,7 +52,7 @@ Omada Health announced GLP-1 Flex Care on March 5, 2026.
|
|||
|
||||
**KB connections:**
|
||||
- [[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]] — Flex Care is a structural response to the cost inflation problem
|
||||
- [[consumer willingness to pay out of pocket for AI-enhanced care is outpacing reimbursement creating a cash-pay adoption pathway]] — this extends the cash-pay logic to the employer level
|
||||
- consumer willingness to pay out of pocket for AI-enhanced care is outpacing reimbursement creating a cash-pay adoption pathway — this extends the cash-pay logic to the employer level
|
||||
- Connects to the covered lives decline archive from Session 31 (DistilINFO: 3.6M → 2.8M)
|
||||
|
||||
**Extraction hints:**
|
||||
|
|
@ -63,6 +63,6 @@ Omada Health announced GLP-1 Flex Care on March 5, 2026.
|
|||
**Context:** GLP-1 Flex Care is Omada's response to employer cost pressure. The innovation is the financial structure (separating program cost from drug cost) rather than clinical innovation. This may be the model that expands GLP-1 behavioral support access even as drug coverage declines.
|
||||
|
||||
## Curator Notes (structured handoff for extractor)
|
||||
PRIMARY CONNECTION: [[GLP-1 receptor agonists are the largest therapeutic category launch...]] — specifically the chronic use cost inflation problem that Flex Care addresses
|
||||
PRIMARY CONNECTION: GLP-1 receptor agonists are the largest therapeutic category launch... — specifically the chronic use cost inflation problem that Flex Care addresses
|
||||
WHY ARCHIVED: Financial structure innovation that directly responds to the covered lives decline documented in prior sessions — new employer purchasing model
|
||||
EXTRACTION HINT: Two extraction paths: (1) new claim about behavioral companion durable outcomes (0.8% weight maintenance vs. 11-12% regain); (2) new claim about employer cash-pay model as structural response to GLP-1 coverage withdrawal
|
||||
|
|
|
|||
|
|
@ -52,7 +52,7 @@ Omada Health Q4 and Full-Year 2025 earnings results:
|
|||
- Second major digital health IPO in 2025 (after Hinge Health)
|
||||
|
||||
## Agent Notes
|
||||
**Why this matters:** Omada achieving first profitable quarter validates the AI-native health company economic model from the KB claim [[AI-native health companies achieve 3-5x the revenue productivity of traditional health services]]. The Q4 profitability flip (from -$8M to positive) after IPO demonstrates that the behavioral digital health model can reach positive unit economics. The 67% GLP-1 persistence vs 47-49% comparison is the clinical differentiation thesis in practice: behavioral support creates better medication adherence.
|
||||
**Why this matters:** Omada achieving first profitable quarter validates the AI-native health company economic model from the KB claim AI-native health companies achieve 3-5x the revenue productivity of traditional health services. The Q4 profitability flip (from -$8M to positive) after IPO demonstrates that the behavioral digital health model can reach positive unit economics. The 67% GLP-1 persistence vs 47-49% comparison is the clinical differentiation thesis in practice: behavioral support creates better medication adherence.
|
||||
|
||||
**What surprised me:** The timing of profitability — Q4 2025, only 6 months post-IPO. Many digital health companies burned cash for years. The combination of revenue growth (+53%) and profitability inflection in the same year is unusual. Also: the GLP-1 Flex Care employer model is clever — it separates the drug cost (employer-burden) from the program cost (employer-buyable), directly addressing the covered lives decline problem (employers want programs without medication cost exposure).
|
||||
|
||||
|
|
@ -60,17 +60,17 @@ Omada Health Q4 and Full-Year 2025 earnings results:
|
|||
|
||||
**KB connections:**
|
||||
- [[AI-native health companies achieve 3-5x the revenue productivity of traditional health services because AI eliminates the linear scaling constraint between headcount and output]] — $260M revenue with behavioral + tech model, now at positive EBITDA, supports this
|
||||
- [[healthcares defensible layer is where atoms become bits]] — Omada is building defensibility through longitudinal behavioral data and outcomes data, not physical sensors (open question for Belief 4)
|
||||
- [[consumer willingness to pay out of pocket for AI-enhanced care is outpacing reimbursement creating a cash-pay adoption pathway]] — GLP-1 Flex Care is precisely this model at the employer level
|
||||
- healthcares defensible layer is where atoms become bits — Omada is building defensibility through longitudinal behavioral data and outcomes data, not physical sensors (open question for Belief 4)
|
||||
- consumer willingness to pay out of pocket for AI-enhanced care is outpacing reimbursement creating a cash-pay adoption pathway — GLP-1 Flex Care is precisely this model at the employer level
|
||||
|
||||
**Extraction hints:**
|
||||
- Potential claim enrichment: [[AI-native health companies achieve 3-5x the revenue productivity...]] — add Omada Q4 2025 profitability as real-world evidence
|
||||
- Potential claim enrichment: AI-native health companies achieve 3-5x the revenue productivity... — add Omada Q4 2025 profitability as real-world evidence
|
||||
- The 67% persistence vs. 47-49% comparison is a quantified behavioral companion program value proof — could be a new claim: "Structured behavioral support programs improve GLP-1 persistence from 47-49% to 67% at 12 months, with proportional improvement in weight outcomes"
|
||||
- The employer cash-pay model (GLP-1 Flex Care) deserves its own claim about how covered lives decline is creating new employer purchasing models
|
||||
|
||||
**Context:** Omada Health is the leading digital health chronic disease management company. IPO validates the model; Q4 profitability is the unit economics proof. The company's GLP-1 expansion (from behavioral companion to prescribing) puts them in direct competition with WW Med+ and Hims/Hers.
|
||||
|
||||
## Curator Notes (structured handoff for extractor)
|
||||
PRIMARY CONNECTION: [[AI-native health companies achieve 3-5x the revenue productivity of traditional health services]] — first real-world profitability data for a leading AI-native health company
|
||||
PRIMARY CONNECTION: AI-native health companies achieve 3-5x the revenue productivity of traditional health services — first real-world profitability data for a leading AI-native health company
|
||||
WHY ARCHIVED: Q4 2025 profitability inflection is a landmark for the digital health model; GLP-1 Flex Care employer cash-pay structure is a novel response to covered lives decline
|
||||
EXTRACTION HINT: Two separate extractions likely needed: (1) profitability as evidence for AI-native unit economics claim; (2) GLP-1 behavioral companion outcomes data (67% persistence) as evidence for behavioral support value claim. Don't conflate.
|
||||
|
|
|
|||
|
|
@ -45,8 +45,8 @@ National launch of the Mental Health Parity Index by The Kennedy Forum, Third Ho
|
|||
**What I expected but didn't find:** State-specific enforcement actions triggered by the Index data. The Index was just launched (April 14), so specific state regulatory responses haven't materialized yet.
|
||||
|
||||
**KB connections:**
|
||||
- [[the mental health supply gap is widening not closing]] — the 16-59% reimbursement gap is the causal mechanism explaining provider opt-out
|
||||
- [[value-based care transitions stall at the payment boundary]] — same structural pattern: payment determines behavior, coverage mandates don't reach payment
|
||||
- the mental health supply gap is widening not closing — the 16-59% reimbursement gap is the causal mechanism explaining provider opt-out
|
||||
- value-based care transitions stall at the payment boundary — same structural pattern: payment determines behavior, coverage mandates don't reach payment
|
||||
- Three-level MHPAEA framework from Session 33 (Level 1: coverage design; Level 1.5: access metrics; Level 2: reimbursement rates)
|
||||
|
||||
**Extraction hints:**
|
||||
|
|
@ -56,6 +56,6 @@ National launch of the Mental Health Parity Index by The Kennedy Forum, Third Ho
|
|||
**Context:** Kennedy Forum is the leading MH parity advocacy organization (Patrick Kennedy, former congressman who co-authored MHPAEA). This Index is explicitly designed to create enforcement pressure through transparency, compensating for federal enforcement withdrawal.
|
||||
|
||||
## Curator Notes (structured handoff for extractor)
|
||||
PRIMARY CONNECTION: [[the mental health supply gap is widening not closing]] — this is the causal mechanism (payment gap driving provider opt-out)
|
||||
PRIMARY CONNECTION: the mental health supply gap is widening not closing — this is the causal mechanism (payment gap driving provider opt-out)
|
||||
WHY ARCHIVED: Provides the most precise national quantification of the reimbursement gap to date, plus establishes insurer-level variation (16-59% range) as a new analytical dimension
|
||||
EXTRACTION HINT: Focus on the range (16-59%, not just the 27.1% average), the ALL 50 STATES finding (universal, not regional), and New York's commitment as the emerging second natural experiment alongside Illinois
|
||||
|
|
|
|||
|
|
@ -41,8 +41,8 @@ New York State, with support from the New York Community Trust, committed to exa
|
|||
**What I expected but didn't find:** A timeline for when the New York analysis will be completed or results published. The Illinois analysis is ongoing — NY presumably will take months to analyze 11M enrollees.
|
||||
|
||||
**KB connections:**
|
||||
- [[the mental health supply gap is widening not closing]] — the two-state natural experiment is the first empirical test of whether state enforcement can close the gap that federal enforcement won't address
|
||||
- [[value-based care transitions stall at the payment boundary]] — state parity enforcement is trying to address the payment boundary from the regulatory side
|
||||
- the mental health supply gap is widening not closing — the two-state natural experiment is the first empirical test of whether state enforcement can close the gap that federal enforcement won't address
|
||||
- value-based care transitions stall at the payment boundary — state parity enforcement is trying to address the payment boundary from the regulatory side
|
||||
- Three-level MHPAEA framework (Sessions 32-33): NY's analysis could generate the level 2 (reimbursement rate) evidence needed for structural enforcement
|
||||
|
||||
**Extraction hints:**
|
||||
|
|
@ -53,6 +53,6 @@ New York State, with support from the New York Community Trust, committed to exa
|
|||
**Context:** Part of a broader state-level compensation pattern for federal enforcement withdrawal. The Parity Index's transparent data architecture is specifically designed to enable state action without federal cooperation.
|
||||
|
||||
## Curator Notes (structured handoff for extractor)
|
||||
PRIMARY CONNECTION: [[the mental health supply gap is widening not closing]] — state enforcement infrastructure being built around the Index
|
||||
PRIMARY CONNECTION: the mental health supply gap is widening not closing — state enforcement infrastructure being built around the Index
|
||||
WHY ARCHIVED: NY is the second major state committing to deep-dive analysis; NY DFS enforcement authority could produce the largest parity enforcement actions to date
|
||||
EXTRACTION HINT: Archive primarily for enrichment of existing claims; the IL + NY natural experiment is the analytical frame but results won't be available for 6-12 months minimum
|
||||
|
|
|
|||
|
|
@ -47,8 +47,8 @@ Real-world 6-month persistence and adherence data from a Medicaid population (JM
|
|||
|
||||
**KB connections:**
|
||||
- [[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]] — cost as #1 discontinuation reason is evidence the chronic use model isn't sticking in low-income populations
|
||||
- [[SDOH interventions show strong ROI but adoption stalls because Z-code documentation remains below 3 percent]] — cost barrier to GLP-1 access is an SDOH problem (financial security = social determinant)
|
||||
- [[medical care explains only 10-20 percent of health outcomes because behavioral social and genetic factors dominate]] — GLP-1 is one of the rare clinical interventions that addresses metabolic disease, but its impact is limited by access barriers that are fundamentally SDOH
|
||||
- SDOH interventions show strong ROI but adoption stalls because Z-code documentation remains below 3 percent — cost barrier to GLP-1 access is an SDOH problem (financial security = social determinant)
|
||||
- medical care explains only 10-20 percent of health outcomes because behavioral social and genetic factors dominate — GLP-1 is one of the rare clinical interventions that addresses metabolic disease, but its impact is limited by access barriers that are fundamentally SDOH
|
||||
|
||||
**Extraction hints:**
|
||||
- Consider enriching existing GLP-1 claim with this Medicaid persistence data and cost barrier finding
|
||||
|
|
@ -58,6 +58,6 @@ Real-world 6-month persistence and adherence data from a Medicaid population (JM
|
|||
**Context:** First major Medicaid-population real-world GLP-1 persistence study. This population (low-income, high chronic burden) is the most affected by the GLP-1 cost problem. The data confirms what was suspected: those who most need the drug are least able to sustain access.
|
||||
|
||||
## Curator Notes (structured handoff for extractor)
|
||||
PRIMARY CONNECTION: [[GLP-1 receptor agonists are the largest therapeutic category launch...]] — specifically the adherence/chronic use model problem
|
||||
PRIMARY CONNECTION: GLP-1 receptor agonists are the largest therapeutic category launch... — specifically the adherence/chronic use model problem
|
||||
WHY ARCHIVED: Medicaid real-world persistence data is the most relevant population for understanding whether GLP-1 can address the population-level chronic disease burden; cost-as-barrier finding challenges any claim that adherence is primarily behavioral
|
||||
EXTRACTION HINT: The structural insight is that cost — not behavior — determines persistence in the lowest-income, highest-chronic-disease population. This has policy implications (drug pricing, Medicaid formulary design) more than clinical implications.
|
||||
|
|
|
|||
|
|
@ -51,9 +51,9 @@ Randomized, double-blind, placebo-controlled clinical trial published in JAMA Ps
|
|||
**What I expected but didn't find:** A clearer mechanism for the addiction effect. The reward salience hypothesis (GLP-1 reduces the hedonic value of alcohol like it reduces food craving) is the leading theory but not confirmed. This matters for whether the effect extends to other substance use disorders (nicotine, cocaine).
|
||||
|
||||
**KB connections:**
|
||||
- [[the mental health supply gap is widening not closing]] — GLP-1 for AUD is a pharmacological bypass of the workforce constraint (no therapist needed for prescribing pathway)
|
||||
- the mental health supply gap is widening not closing — GLP-1 for AUD is a pharmacological bypass of the workforce constraint (no therapist needed for prescribing pathway)
|
||||
- [[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]] — AUD indication could expand the market dramatically beyond metabolic disease
|
||||
- [[medical care explains only 10-20 percent of health outcomes because behavioral social and genetic factors dominate]] — AUD is a behavioral/social health driver; pharmacological treatment of AUD via GLP-1 would address a non-clinical determinant through clinical means
|
||||
- medical care explains only 10-20 percent of health outcomes because behavioral social and genetic factors dominate — AUD is a behavioral/social health driver; pharmacological treatment of AUD via GLP-1 would address a non-clinical determinant through clinical means
|
||||
|
||||
**Extraction hints:**
|
||||
- Strong new claim candidate: "GLP-1 receptor agonists demonstrate NNT 4.3 for alcohol use disorder — superior to all approved AUD medications — extending GLP-1 therapeutic scope from metabolic to behavioral health"
|
||||
|
|
@ -64,6 +64,6 @@ Randomized, double-blind, placebo-controlled clinical trial published in JAMA Ps
|
|||
**Context:** First RCT evidence for a GLP-1 agonist in AUD treatment. Phase 3 trials will determine whether this reaches clinical guidelines. The NNT advantage is significant because existing AUD medications are under-prescribed — semaglutide's broad adoption in obesity/diabetes could translate to dramatically higher AUD treatment penetration.
|
||||
|
||||
## Curator Notes (structured handoff for extractor)
|
||||
PRIMARY CONNECTION: [[GLP-1 receptor agonists are the largest therapeutic category launch in pharmaceutical history...]] — this extends the therapeutic scope claim
|
||||
PRIMARY CONNECTION: GLP-1 receptor agonists are the largest therapeutic category launch in pharmaceutical history... — this extends the therapeutic scope claim
|
||||
WHY ARCHIVED: First RCT evidence of GLP-1 for AUD; NNT 4.3 vs. 7+ approved medications is a category-level finding, not an incremental update
|
||||
EXTRACTION HINT: Write as a new claim scoped to "in adults with comorbid AUD and obesity" — do not generalize to all AUD patients. Acknowledge the cohort study MDD risk signal as challenged_by. Flag for Clay (narrative: substance use has major cultural/social dimensions) and Theseus (behavioral AI safety analog: treating behavioral patterns pharmacologically).
|
||||
|
|
|
|||
|
|
@ -52,9 +52,9 @@ WeightWatchers announced May 1, 2026 that it will offer access to Novo Nordisk's
|
|||
**What I expected but didn't find:** Any signal of CGM integration or wearable integration in WW's clinical transformation. Three sessions of absence confirms this is a deliberate model choice, not a gap being filled.
|
||||
|
||||
**KB connections:**
|
||||
- Belief 4: [[healthcares defensible layer is where atoms become bits]] — WW is testing whether behavioral depth WITHOUT physical data creates a defensible moat
|
||||
- [[GLP-1 receptor agonists are the largest therapeutic category launch in pharmaceutical history]] — WW's entire pivot is built on riding this wave
|
||||
- [[consumer willingness to pay out of pocket for AI-enhanced care is outpacing reimbursement]] — $25/month with insurance is near-consumer pricing for GLP-1 access through WW
|
||||
- Belief 4: healthcares defensible layer is where atoms become bits — WW is testing whether behavioral depth WITHOUT physical data creates a defensible moat
|
||||
- GLP-1 receptor agonists are the largest therapeutic category launch in pharmaceutical history — WW's entire pivot is built on riding this wave
|
||||
- consumer willingness to pay out of pocket for AI-enhanced care is outpacing reimbursement — $25/month with insurance is near-consumer pricing for GLP-1 access through WW
|
||||
|
||||
**Extraction hints:**
|
||||
- This source primarily useful for updating the existing WW-related claim or writing a WW-specific behavioral model claim
|
||||
|
|
@ -64,6 +64,6 @@ WeightWatchers announced May 1, 2026 that it will offer access to Novo Nordisk's
|
|||
**Context:** WW emerged from bankruptcy as a pure-play GLP-1 clinical services company. The brand carries weight (decades of weight management trust) but the legacy model is dying. The clinical pivot is the only viable strategy. Whether behavioral depth without physical data can sustain differentiation vs. Omada (which has outcomes data advantage) is the open question.
|
||||
|
||||
## Curator Notes (structured handoff for extractor)
|
||||
PRIMARY CONNECTION: [[healthcares defensible layer is where atoms become bits]] — this is the Belief 4 generativity test: WW's results without CGM will tell us whether physical data integration is necessary for defensibility
|
||||
PRIMARY CONNECTION: healthcares defensible layer is where atoms become bits — this is the Belief 4 generativity test: WW's results without CGM will tell us whether physical data integration is necessary for defensibility
|
||||
WHY ARCHIVED: Third consecutive confirmation of WW's no-CGM strategy; post-bankruptcy clinical pivot context; oral semaglutide expansion as clinical breadth without physical depth
|
||||
EXTRACTION HINT: Do NOT extract as a standalone WW claim. Archive as evidence for/against the atoms-to-bits thesis in GLP-1 program context. The question is whether behavioral data alone creates defensibility.
|
||||
|
|
|
|||
|
|
@ -54,8 +54,8 @@ CDC National Center for Health Statistics Data Brief No. 548 (January 2026) and
|
|||
|
||||
**KB connections:**
|
||||
- Directly supports Belief 1 grounding: [[Americas declining life expectancy is driven by deaths of despair concentrated in populations and regions most damaged by economic restructuring since the 1980s]]
|
||||
- [[medical care explains only 10-20 percent of health outcomes]] — 76.4% chronic disease prevalence with 90% of $4.9T spending going to chronic disease illustrates the resource misallocation
|
||||
- [[Big Food companies engineer addictive products by hacking evolutionary reward pathways creating a noncommunicable disease epidemic]] — the chronic disease burden has dietary/behavioral roots this data cannot address
|
||||
- medical care explains only 10-20 percent of health outcomes — 76.4% chronic disease prevalence with 90% of $4.9T spending going to chronic disease illustrates the resource misallocation
|
||||
- Big Food companies engineer addictive products by hacking evolutionary reward pathways creating a noncommunicable disease epidemic — the chronic disease burden has dietary/behavioral roots this data cannot address
|
||||
|
||||
**Extraction hints:**
|
||||
- Consider enriching Belief 1's grounding with the 12.4-year healthspan-lifespan gap as a trackable disconfirmation target: "If this number reverses, Belief 1 weakens"
|
||||
|
|
@ -65,6 +65,6 @@ CDC National Center for Health Statistics Data Brief No. 548 (January 2026) and
|
|||
**Context:** NCHS Data Brief No. 548 is an authoritative government source. The healthspan-lifespan gap metric comes from separate academic sources (Columbia Public Health research citing global data). Both converge on the same conclusion: US health quality is declining even as raw survival time recovers from COVID lows.
|
||||
|
||||
## Curator Notes (structured handoff for extractor)
|
||||
PRIMARY CONNECTION: [[Americas declining life expectancy is driven by deaths of despair...]] — extends this with the healthspan-lifespan gap metric
|
||||
PRIMARY CONNECTION: Americas declining life expectancy is driven by deaths of despair... — extends this with the healthspan-lifespan gap metric
|
||||
WHY ARCHIVED: Provides the most quantitatively precise empirical grounding for Belief 1 to date — the 12.4-year sick-years figure is specific enough to track and falsify
|
||||
EXTRACTION HINT: The key claim is the DIVERGENCE between life expectancy (recovering) and healthspan-lifespan gap (worsening) — these are moving in opposite directions and the naive reading of "79.0 years = improvement" would be misleading. The extractor should capture this distinction.
|
||||
|
|
|
|||
|
|
@ -50,9 +50,9 @@ Conflicting evidence on GLP-1 receptor agonists and psychiatric outcomes, from t
|
|||
**What I expected but didn't find:** A clear mechanistic explanation for why semaglutide would both reduce addiction craving AND increase depression risk. The reward salience hypothesis is plausible for addiction but doesn't predict the depression signal. We need mechanistic clarity.
|
||||
|
||||
**KB connections:**
|
||||
- [[the mental health supply gap is widening not closing]] — GLP-1 for AUD could address one behavioral health gap but introduce another if MDD risk is real
|
||||
- the mental health supply gap is widening not closing — GLP-1 for AUD could address one behavioral health gap but introduce another if MDD risk is real
|
||||
- [[centaur team performance depends on role complementarity not mere human-AI combination]] — by analogy, pharmacological intervention in behavioral health requires careful human clinical judgment for patient selection and monitoring
|
||||
- [[human-in-the-loop clinical AI degrades to worse-than-AI-alone]] — psychiatric monitoring for GLP-1 behavioral health deployment requires strong physician oversight
|
||||
- human-in-the-loop clinical AI degrades to worse-than-AI-alone — psychiatric monitoring for GLP-1 behavioral health deployment requires strong physician oversight
|
||||
|
||||
**Extraction hints:**
|
||||
- This source should be cited as the challenged_by evidence for any claim about GLP-1 for AUD
|
||||
|
|
@ -63,6 +63,6 @@ Conflicting evidence on GLP-1 receptor agonists and psychiatric outcomes, from t
|
|||
**Context:** This is an emerging safety signal in a rapidly moving field. The AUD efficacy is from a high-quality RCT; the MDD risk is from observational data. These require different evidentiary weights. The clinical guidance will likely be: GLP-1 for AUD is promising but requires psychiatric screening and monitoring.
|
||||
|
||||
## Curator Notes (structured handoff for extractor)
|
||||
PRIMARY CONNECTION: [[GLP-1 receptor agonists are the largest therapeutic category launch...]] — this complicates the scope claim with a psychiatric safety dimension
|
||||
PRIMARY CONNECTION: GLP-1 receptor agonists are the largest therapeutic category launch... — this complicates the scope claim with a psychiatric safety dimension
|
||||
WHY ARCHIVED: Creates the challenged_by counterevidence for any GLP-1 AUD efficacy claim; the 195% MDD signal is large enough to require acknowledgment
|
||||
EXTRACTION HINT: Do NOT extract as a standalone claim against GLP-1. Use as challenged_by evidence in the AUD efficacy claim. The key scoping work: the RCT population (AUD + obesity) and the cohort population (general GLP-1 recipients) may be different risk profiles.
|
||||
|
|
|
|||
Loading…
Reference in a new issue