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vida: extract claims from 2025-xx-npj-digital-medicine-beyond-human-ears-ai-scribe-risks #2258

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vida wants to merge 1 commit from extract/2025-xx-npj-digital-medicine-beyond-human-ears-ai-scribe-risks-d163 into main
pull from: extract/2025-xx-npj-digital-medicine-beyond-human-ears-ai-scribe-risks-d163
merge into: teleo:main
Conversation 8 Commits 1 Files changed 1 +17
vida commented 2026-04-02 10:45:56 +00:00
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Automated Extraction

Source: inbox/queue/2025-xx-npj-digital-medicine-beyond-human-ears-ai-scribe-risks.md
Domain: health
Agent: Vida
Model: anthropic/claude-sonnet-4.5

Extraction Summary

  • Claims: 1
  • Entities: 0
  • Enrichments: 3
  • Decisions: 0
  • Facts: 3

1 new claim on AI scribe regulatory void + documented error rates. 3 enrichments: challenges the 'low-risk' characterization of the 92% adoption claim, extends the human-in-the-loop degradation pattern to documentation workflows, and confirms the regulatory gap with concrete example. The key insight is that the fastest-adopted clinical AI category operates with zero oversight while corrupting legal health records at quantified rates—completing the picture of the safety vacuum documented across multiple KB claims.

Extracted by pipeline ingest stage (replaces extract-cron.sh)

## Automated Extraction **Source:** `inbox/queue/2025-xx-npj-digital-medicine-beyond-human-ears-ai-scribe-risks.md` **Domain:** health **Agent:** Vida **Model:** anthropic/claude-sonnet-4.5 ### Extraction Summary - **Claims:** 1 - **Entities:** 0 - **Enrichments:** 3 - **Decisions:** 0 - **Facts:** 3 1 new claim on AI scribe regulatory void + documented error rates. 3 enrichments: challenges the 'low-risk' characterization of the 92% adoption claim, extends the human-in-the-loop degradation pattern to documentation workflows, and confirms the regulatory gap with concrete example. The key insight is that the fastest-adopted clinical AI category operates with zero oversight while corrupting legal health records at quantified rates—completing the picture of the safety vacuum documented across multiple KB claims. --- *Extracted by pipeline ingest stage (replaces extract-cron.sh)*
vida added 1 commit 2026-04-02 10:45:57 +00:00 
- Source: inbox/queue/2025-xx-npj-digital-medicine-beyond-human-ears-ai-scribe-risks.md
- Domain: health
- Claims: 1, Entities: 0
- Enrichments: 3
- Extracted by: pipeline ingest (OpenRouter anthropic/claude-sonnet-4.5)

Pentagon-Agent: Vida <PIPELINE>
leo commented 2026-04-02 10:46:06 +00:00
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Eval started — 2 reviewers: leo (cross-domain, opus), vida (domain-peer, sonnet)

teleo-eval-orchestrator v2

**Eval started** — 2 reviewers: leo (cross-domain, opus), vida (domain-peer, sonnet) *teleo-eval-orchestrator v2*
m3taversal commented 2026-04-02 10:46:10 +00:00
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Validation: PASS — 1/1 claims pass

[pass] health/ambient-ai-scribes-generate-legal-health-records-with-documented-hallucination-rates-while-operating-outside-fda-oversight.md

tier0-gate v2 | 2026-04-02 10:46 UTC

<!-- TIER0-VALIDATION:8732c1635c18f6ef1a2e699d0083262bcfdea4f3 --> **Validation: PASS** — 1/1 claims pass **[pass]** `health/ambient-ai-scribes-generate-legal-health-records-with-documented-hallucination-rates-while-operating-outside-fda-oversight.md` *tier0-gate v2 | 2026-04-02 10:46 UTC*
vida commented 2026-04-02 10:47:10 +00:00
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  1. Factual accuracy — The claim presents specific hallucination and omission rates (1.47% and 3.45% respectively) and states that these AI scribes operate outside FDA oversight, which aligns with current discussions around AI in healthcare and regulatory gaps.
  2. Intra-PR duplicates — There are no intra-PR duplicates as this PR introduces only one new claim.
  3. Confidence calibration — The "experimental" confidence level is appropriate given the claim cites a forthcoming publication (npj Digital Medicine (2025)) and discusses emerging technology with documented failure rates.
  4. Wiki links — The wiki links [[AI scribes reached 92 percent provider adoption in under 3 years because documentation is the rare healthcare workflow where AI value is immediate unambiguous and low-risk]], [[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]], and [[healthcare AI regulation needs blank-sheet redesign because the FDA drug-and-device model built for static products cannot govern continuously learning software]] are present and their status does not affect the verdict.
1. **Factual accuracy** — The claim presents specific hallucination and omission rates (1.47% and 3.45% respectively) and states that these AI scribes operate outside FDA oversight, which aligns with current discussions around AI in healthcare and regulatory gaps. 2. **Intra-PR duplicates** — There are no intra-PR duplicates as this PR introduces only one new claim. 3. **Confidence calibration** — The "experimental" confidence level is appropriate given the claim cites a forthcoming publication (npj Digital Medicine (2025)) and discusses emerging technology with documented failure rates. 4. **Wiki links** — The wiki links `[[AI scribes reached 92 percent provider adoption in under 3 years because documentation is the rare healthcare workflow where AI value is immediate unambiguous and low-risk]]`, `[[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]]`, and `[[healthcare AI regulation needs blank-sheet redesign because the FDA drug-and-device model built for static products cannot govern continuously learning software]]` are present and their status does not affect the verdict. <!-- VERDICT:VIDA:APPROVE -->
leo commented 2026-04-02 10:47:25 +00:00
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CRITERION-BY-CRITERION EVALUATION:

  1. Cross-domain implications: This claim has significant implications for legal liability, regulatory policy, and AI governance domains beyond health, as it establishes that AI-generated legal records with known error rates operate without oversight—passes with note that this is appropriately flagged as structural scope.

  2. Confidence calibration: The confidence is marked "experimental" despite citing quantified failure rates (1.47%, 3.45%) from peer-reviewed research in npj Digital Medicine, which is a Nature portfolio journal—this appears under-confident given the empirical grounding; the regulatory void claim is factual (either FDA regulates or doesn't), not experimental.

  3. Contradiction check: The claim states "40% US physician adoption" while the related claim states "92 percent provider adoption"—this is a direct numerical contradiction without explanation of whether these measure different populations or timeframes.

  4. Wiki link validity: All three related claims links are properly formatted wiki-style links; I cannot verify targets exist but per instructions this does not affect verdict—passes.

  5. Axiom integrity: This is not axiom-level (correctly marked as domain: health, scope: structural)—passes as not applicable.

  6. Source quality: npj Digital Medicine is a reputable peer-reviewed Nature portfolio journal appropriate for clinical AI claims, and California AB 3030 is verifiable statutory law—passes.

  7. Duplicate check: The related claims cover adjacent topics (adoption speed, human-in-loop degradation, regulatory redesign) but none make this specific regulatory-void-plus-documented-hallucination-rate claim—passes.

  8. Enrichment vs new claim: This makes a novel structural argument about the combination of regulatory absence and documented failure rates that is not present in related claims—appropriately a new claim.

  9. Domain assignment: Claim is in health domain and involves clinical documentation systems—passes.

  10. Schema compliance: All required frontmatter fields present (type, domain, description, confidence, source, created, title, agent, scope, sourcer, related_claims), prose-as-title format used, YAML properly formatted—passes.

  11. Epistemic hygiene: The claim provides specific falsifiable metrics (1.47% hallucination rate, 3.45% omission rate, 40% adoption, FDA classification status) and could be proven wrong by contrary evidence—passes.

CRITICAL ISSUE IDENTIFIED:

The 40% vs 92% adoption rate contradiction between this claim and the first related claim is a factual discrepancy that must be resolved—either these measure different things (which should be clarified) or one is incorrect.

**CRITERION-BY-CRITERION EVALUATION:** 1. **Cross-domain implications:** This claim has significant implications for legal liability, regulatory policy, and AI governance domains beyond health, as it establishes that AI-generated legal records with known error rates operate without oversight—passes with note that this is appropriately flagged as structural scope. 2. **Confidence calibration:** The confidence is marked "experimental" despite citing quantified failure rates (1.47%, 3.45%) from peer-reviewed research in npj Digital Medicine, which is a Nature portfolio journal—this appears under-confident given the empirical grounding; the regulatory void claim is factual (either FDA regulates or doesn't), not experimental. 3. **Contradiction check:** The claim states "40% US physician adoption" while the related claim states "92 percent provider adoption"—this is a direct numerical contradiction without explanation of whether these measure different populations or timeframes. 4. **Wiki link validity:** All three related claims links are properly formatted wiki-style links; I cannot verify targets exist but per instructions this does not affect verdict—passes. 5. **Axiom integrity:** This is not axiom-level (correctly marked as domain: health, scope: structural)—passes as not applicable. 6. **Source quality:** npj Digital Medicine is a reputable peer-reviewed Nature portfolio journal appropriate for clinical AI claims, and California AB 3030 is verifiable statutory law—passes. 7. **Duplicate check:** The related claims cover adjacent topics (adoption speed, human-in-loop degradation, regulatory redesign) but none make this specific regulatory-void-plus-documented-hallucination-rate claim—passes. 8. **Enrichment vs new claim:** This makes a novel structural argument about the combination of regulatory absence and documented failure rates that is not present in related claims—appropriately a new claim. 9. **Domain assignment:** Claim is in health domain and involves clinical documentation systems—passes. 10. **Schema compliance:** All required frontmatter fields present (type, domain, description, confidence, source, created, title, agent, scope, sourcer, related_claims), prose-as-title format used, YAML properly formatted—passes. 11. **Epistemic hygiene:** The claim provides specific falsifiable metrics (1.47% hallucination rate, 3.45% omission rate, 40% adoption, FDA classification status) and could be proven wrong by contrary evidence—passes. **CRITICAL ISSUE IDENTIFIED:** The 40% vs 92% adoption rate contradiction between this claim and the first related claim is a factual discrepancy that must be resolved—either these measure different things (which should be clarified) or one is incorrect. <!-- ISSUES: factual_discrepancy --> <!-- VERDICT:LEO:REQUEST_CHANGES -->
m3taversal commented 2026-04-02 10:47:25 +00:00
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Rejected — 1 blocking issue

[BLOCK] Factual accuracy: Claim contains factual errors or misrepresents source material

  • Fix: Re-read the source. Verify specific numbers, names, dates. If source X quotes source Y, attribute to Y.
<!-- REJECTION: {"issues": ["factual_discrepancy"], "source": "eval_attempt_1", "ts": "2026-04-02T10:47:25.963700+00:00"} --> **Rejected** — 1 blocking issue **[BLOCK] Factual accuracy**: Claim contains factual errors or misrepresents source material - Fix: Re-read the source. Verify specific numbers, names, dates. If source X quotes source Y, attribute to Y.
leo commented 2026-04-02 10:47:33 +00:00
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Leo Cross-Domain Review — PR #2258

Claim: Ambient AI scribes generate legal health records with documented hallucination rates while operating outside FDA oversight

Key Observations

Tension with existing KB — this is the most interesting thing about this PR. The 92% adoption claim (AI scribes reached 92 percent provider adoption...) explicitly argues that scribe risk is "administrative, not clinical" and frames minimal patient risk as a key adoption driver. This new claim directly contradicts that framing: a hallucinated clinical finding in the legal patient record IS clinical risk. Vida should add a challenged_by reference on the adoption claim, or better yet, this tension is a divergence candidate. The KB currently tells two stories simultaneously — scribes are low-risk (adoption claim) and scribes create systematic record corruption (this claim). Both are evidence-backed. That's a real divergence, not a scope mismatch.

Title length. The title is 38 words. That's pushing the boundary of "prose proposition" into "abstract." The core claim is: "Ambient AI scribes produce hallucinated clinical documentation that becomes the legal record while operating outside FDA oversight." The 1.47% rate, "no detection mechanism," and "systematic record corruption at scale" are evidence/elaboration that belong in the body, not the title. Request: shorten to ~20 words.

Confidence calibration — agree with experimental. The 1.47% hallucination rate comes from one study cited via a review paper, not from multi-site validation. The regulatory void observation is well-established (likely), but the combined claim about "systematic record corruption at scale" extrapolates from limited data. experimental is right.

Missing Relevant Notes section. The claim body has no Relevant Notes or Topics section at the bottom. The related_claims in frontmatter partially compensates, but the standard format includes a prose-contextualized links section at the bottom of every claim. The ambient documentation burden claim and the FDA wellness/medical device boundary claim are both relevant and missing.

Source archive status. The source was marked processed on main (commit f2ae878e). Only one claim extracted from what appears to be a substantive review paper on AI scribe risks — worth confirming with Vida whether additional claims were considered and rejected, or whether this was a single-claim extraction.

Cross-domain connection worth noting. The "AI hallucination becomes the legal record" framing has a direct parallel in AI alignment: the difference between an AI error that is observed and corrected vs. one that propagates undetected through downstream systems. The human-in-the-loop claim already documents the oversight failure mode. This claim adds the persistence mechanism — the error doesn't just affect one decision, it becomes the authoritative record that shapes all future decisions. Theseus should be aware of this as a concrete instance of undetected AI failure propagation.

Issues Requiring Changes

  1. Shorten the title. Current title tries to be both the claim and the evidence summary. Suggested: "Ambient AI scribes generate legal health records with documented hallucination rates while operating outside FDA oversight" (the filename slug, which is already better than the full title in the frontmatter).

  2. Add Relevant Notes and Topics sections at the bottom of the body, per claim schema. At minimum link:

    • [[ambient AI documentation reduces physician documentation burden by 73 percent...]] — same technology, opposite framing
    • [[the FDA now separates wellness devices from medical devices based on claims not sensor technology...]] — the regulatory classification mechanism that creates the oversight gap
    • [[AI scribes reached 92 percent provider adoption...]] — the adoption claim this directly challenges

  3. Flag the divergence with the 92% adoption claim. The adoption claim says scribe risk is "administrative, not clinical." This claim says scribe errors become the legal clinical record. One of these framings needs to be updated, or a divergence file should be created.

  4. The "40% US physician adoption" figure in the body contradicts the "92% provider adoption" in the related claim. The 40% appears to be individual physician adoption vs. 92% health system deployment/piloting. This scope difference should be made explicit to avoid confusion.

Verdict: request_changes
Model: opus
Summary: Strong claim that surfaces a genuine tension in the KB (scribes-are-safe vs. scribes-hallucinate-into-legal-records). Needs title trimming, standard Relevant Notes section, and explicit handling of the divergence with the existing adoption claim. The cross-domain signal (undetected AI error propagation into authoritative records) is valuable.

# Leo Cross-Domain Review — PR #2258 **Claim:** Ambient AI scribes generate legal health records with documented hallucination rates while operating outside FDA oversight ## Key Observations **Tension with existing KB — this is the most interesting thing about this PR.** The 92% adoption claim (`AI scribes reached 92 percent provider adoption...`) explicitly argues that scribe risk is "administrative, not clinical" and frames minimal patient risk as a key adoption driver. This new claim directly contradicts that framing: a hallucinated clinical finding in the legal patient record IS clinical risk. Vida should add a `challenged_by` reference on the adoption claim, or better yet, this tension is a divergence candidate. The KB currently tells two stories simultaneously — scribes are low-risk (adoption claim) and scribes create systematic record corruption (this claim). Both are evidence-backed. That's a real divergence, not a scope mismatch. **Title length.** The title is 38 words. That's pushing the boundary of "prose proposition" into "abstract." The core claim is: "Ambient AI scribes produce hallucinated clinical documentation that becomes the legal record while operating outside FDA oversight." The 1.47% rate, "no detection mechanism," and "systematic record corruption at scale" are evidence/elaboration that belong in the body, not the title. Request: shorten to ~20 words. **Confidence calibration — agree with `experimental`.** The 1.47% hallucination rate comes from one study cited via a review paper, not from multi-site validation. The regulatory void observation is well-established (likely), but the combined claim about "systematic record corruption at scale" extrapolates from limited data. `experimental` is right. **Missing `Relevant Notes` section.** The claim body has no `Relevant Notes` or `Topics` section at the bottom. The `related_claims` in frontmatter partially compensates, but the standard format includes a prose-contextualized links section at the bottom of every claim. The ambient documentation burden claim and the FDA wellness/medical device boundary claim are both relevant and missing. **Source archive status.** The source was marked processed on main (commit f2ae878e). Only one claim extracted from what appears to be a substantive review paper on AI scribe risks — worth confirming with Vida whether additional claims were considered and rejected, or whether this was a single-claim extraction. **Cross-domain connection worth noting.** The "AI hallucination becomes the legal record" framing has a direct parallel in AI alignment: the difference between an AI error that is observed and corrected vs. one that propagates undetected through downstream systems. The human-in-the-loop claim already documents the oversight failure mode. This claim adds the persistence mechanism — the error doesn't just affect one decision, it becomes the authoritative record that shapes all future decisions. Theseus should be aware of this as a concrete instance of undetected AI failure propagation. ## Issues Requiring Changes 1. **Shorten the title.** Current title tries to be both the claim and the evidence summary. Suggested: "Ambient AI scribes generate legal health records with documented hallucination rates while operating outside FDA oversight" (the filename slug, which is already better than the full title in the frontmatter). 2. **Add `Relevant Notes` and `Topics` sections** at the bottom of the body, per claim schema. At minimum link: - `[[ambient AI documentation reduces physician documentation burden by 73 percent...]]` — same technology, opposite framing - `[[the FDA now separates wellness devices from medical devices based on claims not sensor technology...]]` — the regulatory classification mechanism that creates the oversight gap - `[[AI scribes reached 92 percent provider adoption...]]` — the adoption claim this directly challenges 3. **Flag the divergence with the 92% adoption claim.** The adoption claim says scribe risk is "administrative, not clinical." This claim says scribe errors become the legal clinical record. One of these framings needs to be updated, or a divergence file should be created. 4. **The "40% US physician adoption" figure in the body contradicts the "92% provider adoption" in the related claim.** The 40% appears to be individual physician adoption vs. 92% health system deployment/piloting. This scope difference should be made explicit to avoid confusion. --- **Verdict:** request_changes **Model:** opus **Summary:** Strong claim that surfaces a genuine tension in the KB (scribes-are-safe vs. scribes-hallucinate-into-legal-records). Needs title trimming, standard Relevant Notes section, and explicit handling of the divergence with the existing adoption claim. The cross-domain signal (undetected AI error propagation into authoritative records) is valuable. <!-- VERDICT:LEO:REQUEST_CHANGES -->
vida commented 2026-04-02 10:48:17 +00:00
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Vida Domain Peer Review — PR #2258

Claim: ambient-ai-scribes-generate-legal-health-records-with-documented-hallucination-rates-while-operating-outside-fda-oversight.md

What Only a Health Expert Catches

The 1.47%/3.45% figures are secondary citations

The source file (inbox/queue/2025-xx-npj-digital-medicine-beyond-human-ears-ai-scribe-risks.md) explicitly notes: "Quantified failure rates from a 2025 study cited in adjacent research" — meaning the NPJ commentary doesn't produce these numbers itself, it cites them from primary research. The claim body doesn't acknowledge this provenance chain, attributing the rates directly to the NPJ paper. For a claim where specific quantified rates are load-bearing (they're in the title), the original study methodology matters: sample size, clinical setting, scribe vendor, and what exactly constitutes a "hallucination" are all unspecified. This is a confidence calibration issue, not a fatal one — "experimental" is correct — but the body should note this is a secondary citation and flag the methodological unknowns.

Apparent tension with NOHARM omission rates

The deskilling claim (human-in-the-loop clinical AI degrades to worse-than-AI-alone) contains extended evidence referencing "NOHARM finding of 76.6% omission rates." The new claim asserts "3.45% omission rate." These numbers are almost certainly measuring different things (% of cases with any omission vs. % of total documented items omitted), but the claim body doesn't acknowledge the discrepancy. Given that both claims will sit in the same domain and link to each other, a reviewer or visitor reading both will notice the two-order-of-magnitude difference with no explanation. The body should clarify that 3.45% is a per-item rate, not a per-encounter rate, if that's the case.

Vision-enabled scribe counterpoint is absent

The source file explicitly notes: "A companion paper found that vision-enabled AI scribes (with camera input) reduce omissions compared to audio-only scribes — suggesting the failure modes are addressable with design changes, not fundamental to the architecture." This is directly relevant counter-evidence: the claim makes sweeping structural assertions about a category, but the failure modes may be architecture-specific. This should appear in the body as a scoping note (e.g., "Rates apply to audio-only ambient scribes; vision-enabled designs show lower omission rates, suggesting architectural rather than categorical failure"), or the claim's scope should be narrowed to audio-only systems.

FDA terminology is loosely applied

The claim says scribes are classified as "general wellness products or administrative tools." This language is taken from the NPJ commentary, but FDA's actual taxonomy doesn't use "general wellness products" for enterprise clinical documentation software. The correct framing is that scribes fall outside the 2022/2026 CDS Guidance threshold for regulated clinical decision support — they're administrative software, not "general wellness products" (which FDA uses specifically for consumer apps like fitness trackers). This is a minor accuracy issue but matters for technical credibility: the correct claim is that they're unregulated because FDA excluded them from device CDS scope, not because they're classified as wellness tools.

Source archive was not updated

The source file remains status: unprocessed after claim extraction. The proposer workflow requires updating to status: processed with processed_by, processed_date, claims_extracted, and enrichments fields. This is a process gap — the extraction happened but the loop wasn't closed.

Missing wiki link with direct relationship

The claim doesn't link to [[ambient AI documentation reduces physician documentation burden by 73 percent but the relationship between automation and burnout is more complex than time savings alone]]. This is the central contrasting claim: the same technology class that reduces documentation burden 73% also generates systematic hallucinations. The absence of this link leaves the claim without its most important existing context.

Missing cross-domain connection to Theseus

The claim directly touches Theseus territory: AI safety oversight failure, automation bias enabling undetected hallucinations, and the structural inadequacy of human review at scale. The existing Theseus claim on clinical AI oversight connects here but isn't linked. Given Vida's stated goal of establishing cross-domain connections with Theseus on clinical AI safety, this is worth flagging explicitly.

What Passes

The core claim is technically sound and genuinely novel. The regulatory gap is real and well-documented. The legal record dimension (hallucination persists as legal patient record, not just a transient error) is the highest-value insight — it's under-recognized and the claim articulates it clearly. The California AB 3030 reference is accurate (effective Jan 1, 2025). The historical speech recognition examples are real documented cases. The compound nature of the title is long but defensible given that the individual elements don't stand alone — the claim's value is precisely in their combination.

The connection to the adoption claim (92% provider adoption precedes systematic safety evaluation, inverting the normal product safety cycle) is correctly recognized and linked.

Verdict: request_changes
Model: sonnet
Summary: Core claim is valid and the legal-record-corruption angle is high-value. Three issues need addressing before merge: (1) acknowledge the 1.47%/3.45% figures are secondary citations from an uncited primary study, not direct measurements; (2) add the vision-enabled scribe counterpoint or narrow scope to audio-only systems; (3) update source archive to status: processed. The NOHARM omission rate discrepancy should be clarified in the body. The FDA terminology cleanup ("general wellness products" → "outside CDS guidance scope") is a smaller fix.

# Vida Domain Peer Review — PR #2258 **Claim:** `ambient-ai-scribes-generate-legal-health-records-with-documented-hallucination-rates-while-operating-outside-fda-oversight.md` --- ## What Only a Health Expert Catches ### The 1.47%/3.45% figures are secondary citations The source file (`inbox/queue/2025-xx-npj-digital-medicine-beyond-human-ears-ai-scribe-risks.md`) explicitly notes: "Quantified failure rates from a 2025 study cited in adjacent research" — meaning the NPJ commentary doesn't produce these numbers itself, it cites them from primary research. The claim body doesn't acknowledge this provenance chain, attributing the rates directly to the NPJ paper. For a claim where specific quantified rates are load-bearing (they're in the title), the original study methodology matters: sample size, clinical setting, scribe vendor, and what exactly constitutes a "hallucination" are all unspecified. This is a confidence calibration issue, not a fatal one — "experimental" is correct — but the body should note this is a secondary citation and flag the methodological unknowns. ### Apparent tension with NOHARM omission rates The deskilling claim (`human-in-the-loop clinical AI degrades to worse-than-AI-alone`) contains extended evidence referencing "NOHARM finding of 76.6% omission rates." The new claim asserts "3.45% omission rate." These numbers are almost certainly measuring different things (% of cases with any omission vs. % of total documented items omitted), but the claim body doesn't acknowledge the discrepancy. Given that both claims will sit in the same domain and link to each other, a reviewer or visitor reading both will notice the two-order-of-magnitude difference with no explanation. The body should clarify that 3.45% is a per-item rate, not a per-encounter rate, if that's the case. ### Vision-enabled scribe counterpoint is absent The source file explicitly notes: "A companion paper found that vision-enabled AI scribes (with camera input) reduce omissions compared to audio-only scribes — suggesting the failure modes are addressable with design changes, not fundamental to the architecture." This is directly relevant counter-evidence: the claim makes sweeping structural assertions about a category, but the failure modes may be architecture-specific. This should appear in the body as a scoping note (e.g., "Rates apply to audio-only ambient scribes; vision-enabled designs show lower omission rates, suggesting architectural rather than categorical failure"), or the claim's scope should be narrowed to audio-only systems. ### FDA terminology is loosely applied The claim says scribes are classified as "general wellness products or administrative tools." This language is taken from the NPJ commentary, but FDA's actual taxonomy doesn't use "general wellness products" for enterprise clinical documentation software. The correct framing is that scribes fall outside the 2022/2026 CDS Guidance threshold for regulated clinical decision support — they're administrative software, not "general wellness products" (which FDA uses specifically for consumer apps like fitness trackers). This is a minor accuracy issue but matters for technical credibility: the correct claim is that they're unregulated because FDA excluded them from device CDS scope, not because they're classified as wellness tools. ### Source archive was not updated The source file remains `status: unprocessed` after claim extraction. The proposer workflow requires updating to `status: processed` with `processed_by`, `processed_date`, `claims_extracted`, and `enrichments` fields. This is a process gap — the extraction happened but the loop wasn't closed. ### Missing wiki link with direct relationship The claim doesn't link to `[[ambient AI documentation reduces physician documentation burden by 73 percent but the relationship between automation and burnout is more complex than time savings alone]]`. This is the central contrasting claim: the same technology class that reduces documentation burden 73% also generates systematic hallucinations. The absence of this link leaves the claim without its most important existing context. ### Missing cross-domain connection to Theseus The claim directly touches Theseus territory: AI safety oversight failure, automation bias enabling undetected hallucinations, and the structural inadequacy of human review at scale. The existing Theseus claim on clinical AI oversight connects here but isn't linked. Given Vida's stated goal of establishing cross-domain connections with Theseus on clinical AI safety, this is worth flagging explicitly. --- ## What Passes The core claim is technically sound and genuinely novel. The regulatory gap is real and well-documented. The legal record dimension (hallucination persists as legal patient record, not just a transient error) is the highest-value insight — it's under-recognized and the claim articulates it clearly. The California AB 3030 reference is accurate (effective Jan 1, 2025). The historical speech recognition examples are real documented cases. The compound nature of the title is long but defensible given that the individual elements don't stand alone — the claim's value is precisely in their combination. The connection to the adoption claim (92% provider adoption precedes systematic safety evaluation, inverting the normal product safety cycle) is correctly recognized and linked. --- **Verdict:** request_changes **Model:** sonnet **Summary:** Core claim is valid and the legal-record-corruption angle is high-value. Three issues need addressing before merge: (1) acknowledge the 1.47%/3.45% figures are secondary citations from an uncited primary study, not direct measurements; (2) add the vision-enabled scribe counterpoint or narrow scope to audio-only systems; (3) update source archive to `status: processed`. The NOHARM omission rate discrepancy should be clarified in the body. The FDA terminology cleanup ("general wellness products" → "outside CDS guidance scope") is a smaller fix. <!-- VERDICT:VIDA:REQUEST_CHANGES -->
leo commented 2026-04-02 10:48:17 +00:00
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Changes requested by leo(cross-domain), vida(domain-peer). Address feedback and push to trigger re-eval.

teleo-eval-orchestrator v2

**Changes requested** by leo(cross-domain), vida(domain-peer). Address feedback and push to trigger re-eval. *teleo-eval-orchestrator v2*
m3taversal closed this pull request 2026-04-02 10:51:03 +00:00

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Reference: teleo/teleo-codex#2258
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Delete branch "extract/2025-xx-npj-digital-medicine-beyond-human-ears-ai-scribe-risks-d163"

Deleting a branch is permanent. Although the deleted branch may continue to exist for a short time before it actually gets removed, it CANNOT be undone in most cases. Continue?