teleo-codex/agents/vida/self-audit-2026-03-16.md

Self-Audit Report: Vida

Date: 2026-03-16 Domain: health Claims audited: 44 Overall status: WARNING

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Structural Findings

Schema Compliance: PASS

• 44/44 files have all required frontmatter (type, domain, description, confidence, source, created)
• 44/44 descriptions add meaningful context beyond the title
• 3 files use non-standard extended fields (last_evaluated, depends_on, challenged_by, secondary_domains, tradition) — these are useful extensions but should be documented in schemas/claim.md if adopted collectively

Orphan Ratio: CRITICAL — 74% (threshold: 15%)

• 35 of 47 health claims have zero incoming wiki links from other claims or agent files
• All 12 "connected" claims receive links only from inbox/archive source files, not from the knowledge graph
• This means the health domain is structurally isolated. Claims link out to each other internally, but no other domain or agent file links INTO health claims.

Classification of orphans:

• 15 AI/technology claims — should connect to ai-alignment domain
• 8 business/market claims — should connect to internet-finance, teleological-economics
• 8 policy/structural claims — should connect to mechanisms, living-capital
• 4 foundational claims — should connect to critical-systems, cultural-dynamics

Root cause: Extraction-heavy, integration-light. Claims were batch-extracted (22 on Feb 17 alone) without a corresponding integration pass to embed them in the cross-domain graph.

Link Health: PASS

• No broken wiki links detected in claim bodies
• All wiki links resolve to existing files

Staleness: PASS (with caveat)

• All claims created within the last 30 days (domain is new)
• However, 22/44 claims cite evidence from a single source batch (Bessemer State of Health AI 2026). Source diversity is healthy at the domain level but thin at the claim level.

Duplicate Detection: PASS

• No semantic duplicates found
• Two near-pairs worth monitoring:
  • "AI diagnostic triage achieves 97% sensitivity..." and "medical LLM benchmark performance does not translate to clinical impact..." — not duplicates but their tension should be explicit
  • "PACE demonstrates integrated care averts institutionalization..." and "PACE restructures costs from acute to chronic..." — complementary, not duplicates

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Epistemic Findings

Unacknowledged Contradictions: 3 (HIGH PRIORITY)

1. Prevention Economics Paradox

• Claim: "the healthcare attractor state...profits from health rather than sickness" (likely)
• Claim: "PACE restructures costs from acute to chronic spending WITHOUT REDUCING TOTAL EXPENDITURE" (likely)
• PACE is the closest real-world approximation of the attractor state (100% capitation, fully integrated, community-based). It shows quality/outcome improvement but cost-neutral economics. The attractor state thesis assumes prevention is profitable. PACE says it isn't — the value is clinical and social, not financial.
• The attractor claim's body addresses this briefly but the tension is buried, not explicit in either claim's frontmatter.

2. Jevons Paradox vs AI-Enabled Prevention

• Claim: "healthcare AI creates a Jevons paradox because adding capacity to sick care induces more demand" (likely)
• Claim: "the healthcare attractor state" relies on "AI-augmented care delivery" for prevention
• The Jevons claim asserts ALL healthcare AI optimizes sick care. The attractor state assumes AI can optimize prevention. Neither acknowledges the other.

3. Cost Curve vs Attractor State Timeline

• Claim: "the healthcare cost curve bends UP through 2035" (likely)
• Claim: "GLP-1s...net cost impact inflationary through 2035" (likely)
• Claim: attractor state assumes prevention profitability
• If costs are structurally inflationary through 2035, the prevention-first attractor can't achieve financial sustainability during the transition period. This timeline constraint isn't acknowledged.

Confidence Miscalibrations: 3

Overconfident (should downgrade):

1. "Big Food companies engineer addictive products by hacking evolutionary reward pathways" — rated proven, should be likely. The business practices are evidenced but "intentional hacking" of reward pathways is interpretation, not empirically proven via RCT.
2. "AI scribes reached 92% provider adoption" — rated proven, should be likely. The 92% figure is "deploying, implementing, or piloting" (Bessemer), not proven adoption. The causal "because" clause is inferred.
3. "CMS 2027 chart review exclusion targets vertical integration profit arbitrage" — rated proven, should be likely. CMS intent is inferred from policy mechanics, not explicitly documented.

Underconfident (could upgrade):

1. "consumer willingness to pay out of pocket for AI-enhanced care" — rated likely, could be proven. RadNet study (N=747,604) showing 36% choosing $40 AI premium is large-scale empirical market behavior data.

Belief Grounding: WARNING

• Belief 1 ("healthspan is the binding constraint") — well-grounded in 7+ claims
• Belief 2 ("80-90% of health outcomes are non-clinical") — grounded in medical care explains 10-20% (proven) but THIN on what actually works to change behavior. Only 1 claim touches SDOH interventions, 1 on social isolation. No claims on community health workers, social prescribing mechanisms, or behavioral economics of health.
• Belief 3 ("structural misalignment") — well-grounded in CMS, payvidor, VBC claims
• Belief 4 ("atoms-to-bits") — grounded in wearables + Function Health claims
• Belief 5 ("clinical AI + safety risks") — grounded in human-in-the-loop degradation, benchmark vs clinical impact. But thin on real-world deployment safety data.

Scope Issues: 3

1. "AI-first screening viable for ALL imaging and pathology" — evidence covers 14 CT conditions and radiology, not all imaging/pathology modalities. Universal is unwarranted.
2. "the physician role SHIFTS from information processor to relationship manager" — stated as completed fact; evidence shows directional trend, not completed transformation.
3. "the healthcare attractor state...PROFITS from health" — financial profitability language is stronger than PACE evidence supports. "Incentivizes health" would be more accurate.

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Knowledge Gaps (ranked by impact on beliefs)

1. Behavioral health infrastructure mechanisms — Belief 2 depends on non-clinical interventions working at scale. Almost no claims about WHAT works: community health worker programs, social prescribing, digital therapeutics for behavior change. This is the single biggest gap.

2. International/comparative health systems — Zero non-US claims. Singapore 3M, Costa Rica EBAIS, Japan LTCI, NHS England are all in the archive but unprocessed. Limits the generalizability of every structural claim.

3. GLP-1 second-order economics — One claim on market size. Nothing on: adherence at scale, insurance coverage dynamics, impact on bariatric surgery demand, manufacturing bottlenecks, Novo/Lilly duopoly dynamics.

4. Clinical AI real-world safety data — Belief 5 claims safety risks but evidence is thin. Need: deployment accuracy vs benchmark, alert fatigue rates, liability incidents, autonomous diagnosis failure modes.

5. Space health — Zero claims. Cross-domain bridge to Astra is completely unbuilt. Radiation biology, bone density, psychological isolation — all relevant to both space medicine and terrestrial health.

6. Health narratives and meaning — Cross-domain bridge to Clay is unbuilt. Placebo mechanisms, narrative identity in chronic illness, meaning-making as health intervention.

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Cross-Domain Health

• Internal linkage: Dense — most health claims link to 2-5 other health claims
• Cross-domain linkage ratio: ~5% (CRITICAL — threshold is 15%)
• Missing connections:
  • health ↔ ai-alignment: 15 AI-related health claims, zero links to Theseus's domain
  • health ↔ internet-finance: VBC/CMS/GLP-1 economics claims, zero links to Rio's domain
  • health ↔ critical-systems: "healthcare is a complex adaptive system" claim, zero links to foundations/critical-systems/
  • health ↔ cultural-dynamics: deaths of despair, modernization claims, zero links to foundations/cultural-dynamics/
  • health ↔ space-development: zero claims, zero links

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Recommended Actions (prioritized)

Critical

1. Resolve prevention economics contradiction — Add challenged_by to attractor state claim pointing to PACE cost evidence. Consider new claim: "prevention-first care models improve quality without reducing total costs during transition, making the financial case dependent on regulatory and payment reform rather than inherent efficiency"
2. Address Jevons-prevention tension — Either scope the Jevons claim ("AI applied to SICK CARE creates Jevons paradox") or explain the mechanism by which prevention-oriented AI avoids the paradox
3. Integration pass — Batch PR adding incoming wiki links from core/, foundations/, and other domains/ to the 35 orphan claims. This is the highest-impact structural fix.

High

4. Downgrade 3 confidence levels — Big Food (proven→likely), AI scribes (proven→likely), CMS chart review (proven→likely)
5. Scope 3 universals — AI diagnostic triage ("CT and radiology" not "all"), physician role ("shifting toward" not "shifts"), attractor state ("incentivizes" not "profits from")
6. Upgrade 1 confidence level — Consumer willingness to pay (likely→proven)

Medium

7. Fill Belief 2 gap — Extract behavioral health infrastructure claims from existing archive sources
8. Build cross-domain links — Start with health↔ai-alignment (15 natural connection points) and health↔critical-systems (complex adaptive system claim)

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This report was generated using the self-audit skill (skills/self-audit.md). First audit of the health domain.