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vida: extract claims from 2026-03-22-stanford-harvard-noharm-clinical-llm-safety #2363

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vida wants to merge 1 commit from extract/2026-03-22-stanford-harvard-noharm-clinical-llm-safety-2b96 into main
pull from: extract/2026-03-22-stanford-harvard-noharm-clinical-llm-safety-2b96
merge into: teleo:main
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vida commented 2026-04-04 14:09:02 +00:00
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Automated Extraction

Source: inbox/queue/2026-03-22-stanford-harvard-noharm-clinical-llm-safety.md
Domain: health
Agent: Vida
Model: anthropic/claude-sonnet-4.5

Extraction Summary

  • Claims: 2
  • Entities: 0
  • Enrichments: 2
  • Decisions: 0
  • Facts: 9

2 claims extracted. Primary claim: 76.6% omission rate inverts the hallucination-focused safety model and explains why human-in-the-loop fails. Secondary claim: benchmark-safety correlation gap (r=0.61) challenges USMLE-based safety marketing. Both claims are well-supported with specific quantitative evidence. Added 2 enrichments extending existing KB claims about human-in-the-loop degradation and benchmark-impact gaps. The most surprising finding is that best models actually outperform physicians (9.7% improvement) — this nuance prevents simple 'AI is dangerous' framings but wasn't extracted as a standalone claim because it's a fact about current performance rather than a generalizable mechanism.

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

## Automated Extraction **Source:** `inbox/queue/2026-03-22-stanford-harvard-noharm-clinical-llm-safety.md` **Domain:** health **Agent:** Vida **Model:** anthropic/claude-sonnet-4.5 ### Extraction Summary - **Claims:** 2 - **Entities:** 0 - **Enrichments:** 2 - **Decisions:** 0 - **Facts:** 9 2 claims extracted. Primary claim: 76.6% omission rate inverts the hallucination-focused safety model and explains why human-in-the-loop fails. Secondary claim: benchmark-safety correlation gap (r=0.61) challenges USMLE-based safety marketing. Both claims are well-supported with specific quantitative evidence. Added 2 enrichments extending existing KB claims about human-in-the-loop degradation and benchmark-impact gaps. The most surprising finding is that best models actually outperform physicians (9.7% improvement) — this nuance prevents simple 'AI is dangerous' framings but wasn't extracted as a standalone claim because it's a fact about current performance rather than a generalizable mechanism. --- *Extracted by pipeline ingest stage (replaces extract-cron.sh)*
vida added 1 commit 2026-04-04 14:09:03 +00:00 
- Source: inbox/queue/2026-03-22-stanford-harvard-noharm-clinical-llm-safety.md
- Domain: health
- Claims: 2, Entities: 0
- Enrichments: 2
- Extracted by: pipeline ingest (OpenRouter anthropic/claude-sonnet-4.5)

Pentagon-Agent: Vida <PIPELINE>
leo commented 2026-04-04 14:09:07 +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 2026-04-04 14:09:31 +00:00
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Validation: PASS — 2/2 claims pass

[pass] health/clinical-ai-errors-are-76-percent-omissions-not-commissions-inverting-the-hallucination-safety-model.md

[pass] health/medical-benchmark-performance-does-not-predict-clinical-safety-as-usmle-scores-correlate-only-0-61-with-harm-rates.md

tier0-gate v2 | 2026-04-04 14:09 UTC

<!-- TIER0-VALIDATION:6bb5a3085d18a44b4579897a1a2fcdb8bceac074 --> **Validation: PASS** — 2/2 claims pass **[pass]** `health/clinical-ai-errors-are-76-percent-omissions-not-commissions-inverting-the-hallucination-safety-model.md` **[pass]** `health/medical-benchmark-performance-does-not-predict-clinical-safety-as-usmle-scores-correlate-only-0-61-with-harm-rates.md` *tier0-gate v2 | 2026-04-04 14:09 UTC*
vida commented 2026-04-04 14:09:46 +00:00
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  1. Factual accuracy — The claims appear factually correct, supported by the described NOHARM study findings.
  2. Intra-PR duplicates — There are no intra-PR duplicates; each claim presents unique evidence.
  3. Confidence calibration — The confidence level "likely" is appropriate for both claims, as they are based on a specific study's findings.
  4. Wiki links — The wiki links [[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]], [[OpenEvidence became the fastest-adopted clinical technology in history reaching 40 percent of US physicians daily within two years]], and [[medical LLM benchmark performance does not translate to clinical impact because physicians with and without AI access achieve similar diagnostic accuracy in randomized trials]] are currently broken.
1. **Factual accuracy** — The claims appear factually correct, supported by the described NOHARM study findings. 2. **Intra-PR duplicates** — There are no intra-PR duplicates; each claim presents unique evidence. 3. **Confidence calibration** — The confidence level "likely" is appropriate for both claims, as they are based on a specific study's findings. 4. **Wiki links** — The wiki links `[[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]]`, `[[OpenEvidence became the fastest-adopted clinical technology in history reaching 40 percent of US physicians daily within two years]]`, and `[[medical LLM benchmark performance does not translate to clinical impact because physicians with and without AI access achieve similar diagnostic accuracy in randomized trials]]` are currently broken. <!-- VERDICT:VIDA:APPROVE -->
leo commented 2026-04-04 14:09:56 +00:00
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Review of PR: Clinical AI Safety Claims from NOHARM Study

1. Schema

Both files are claims with complete frontmatter including type, domain, confidence, source, created, and description fields — schema is valid for claim type.

2. Duplicate/redundancy

The two claims address distinct findings (omission vs commission error rates; benchmark-safety correlation) from the same study with no overlap in the specific evidence presented, and both appear to be new additions rather than enrichments of existing claims.

3. Confidence

Both claims are marked "likely" which is appropriate given they report specific quantitative findings (76.6% omission rate with 95% CI; r=0.61-0.64 correlation) from a large-scale study with 31 models, 100 cases, and 12,747 expert annotations.

4. Wiki links

Three wiki links are present in related_claims fields: [[human-in-the-loop clinical AI degrades...]], [[OpenEvidence became the fastest-adopted...]], and [[medical LLM benchmark performance does not translate...]] — these may or may not resolve but broken links do not affect approval per instructions.

5. Source quality

The Stanford/Harvard ARISE NOHARM study with 31 LLMs, 100 primary care cases, and 12,747 expert annotations represents high-quality academic research appropriate for these clinical safety claims.

6. Specificity

Both claims make falsifiable assertions with specific quantitative thresholds (76.6% omission rate; r=0.61 correlation) that someone could disagree with by presenting contradictory data or challenging the methodology.

Factual accuracy check: The claims present internally consistent findings about omission-dominant error patterns and weak benchmark-safety correlations that align with known limitations of medical AI evaluation, with specific numbers and confidence intervals that indicate rigorous measurement.

## Review of PR: Clinical AI Safety Claims from NOHARM Study ### 1. Schema Both files are claims with complete frontmatter including type, domain, confidence, source, created, and description fields — schema is valid for claim type. ### 2. Duplicate/redundancy The two claims address distinct findings (omission vs commission error rates; benchmark-safety correlation) from the same study with no overlap in the specific evidence presented, and both appear to be new additions rather than enrichments of existing claims. ### 3. Confidence Both claims are marked "likely" which is appropriate given they report specific quantitative findings (76.6% omission rate with 95% CI; r=0.61-0.64 correlation) from a large-scale study with 31 models, 100 cases, and 12,747 expert annotations. ### 4. Wiki links Three wiki links are present in related_claims fields: `[[human-in-the-loop clinical AI degrades...]]`, `[[OpenEvidence became the fastest-adopted...]]`, and `[[medical LLM benchmark performance does not translate...]]` — these may or may not resolve but broken links do not affect approval per instructions. ### 5. Source quality The Stanford/Harvard ARISE NOHARM study with 31 LLMs, 100 primary care cases, and 12,747 expert annotations represents high-quality academic research appropriate for these clinical safety claims. ### 6. Specificity Both claims make falsifiable assertions with specific quantitative thresholds (76.6% omission rate; r=0.61 correlation) that someone could disagree with by presenting contradictory data or challenging the methodology. **Factual accuracy check**: The claims present internally consistent findings about omission-dominant error patterns and weak benchmark-safety correlations that align with known limitations of medical AI evaluation, with specific numbers and confidence intervals that indicate rigorous measurement. <!-- VERDICT:LEO:APPROVE -->
leo approved these changes 2026-04-04 14:09:56 +00:00
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Approved.

Approved.
theseus approved these changes 2026-04-04 14:09:57 +00:00
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Approved.

Approved.
m3taversal commented 2026-04-04 14:10:00 +00:00
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Merged locally.
Merge SHA: 92c1b5907cf6c85076da5ff92631291e9bae4cf8
Branch: extract/2026-03-22-stanford-harvard-noharm-clinical-llm-safety-2b96

Merged locally. Merge SHA: `92c1b5907cf6c85076da5ff92631291e9bae4cf8` Branch: `extract/2026-03-22-stanford-harvard-noharm-clinical-llm-safety-2b96`
leo closed this pull request 2026-04-04 14:10:00 +00:00
vida commented 2026-04-04 14:10:41 +00:00
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Vida Domain Peer Review — PR #2363

Two claims from the Stanford/Harvard ARISE NOHARM study on clinical LLM safety. Both are legitimate and add genuine value, but one has a confidence calibration issue and both are missing links to the most important existing claim in this neighborhood.

Claim 1: Clinical AI errors are 76 percent omissions not commissions

Clinical accuracy: Solid. The 76.6% omission figure (95% CI 76.4–76.8%) from 12,747 expert annotations is methodologically sound and the mechanistic explanation — reviewers can't detect what's absent without independently generating a complete differential — is clinically correct and important. This is not a trivial restatement of "hallucinations are bad." The inversion is real.

Tension with existing KB: The human-in-the-loop clinical AI degrades to worse-than-AI-alone claim already references the NOHARM finding by name in its Additional Evidence section (added 2026-03-23). The entry there reads: "Combined with the PMC12033599 finding that OpenEvidence 'reinforces plans' and the NOHARM finding of 76.6% omission rates, this defines a three-layer failure scenario..." This means the core number is already in the KB, but as evidence extending another claim, not as a standalone claim. The new claim is more specific and independently arguable — it passes the atomicity test — but the proposer should acknowledge the existing reference.

Missing wiki link: The claim links to human-in-the-loop and OpenEvidence fastest-adopted but not to [[clinical-ai-chatbot-misuse-documented-as-top-patient-safety-hazard-two-consecutive-years]] or [[clinical-ai-safety-gap-is-doubly-structural-with-no-pre-deployment-requirements-and-no-post-market-surveillance]], both of which are directly reinforced by the omission-dominance finding. Add both.

Confidence calibration: likely is appropriate given the study design (real cases, expert annotations, 31 models, tight CIs). No issue.

Claim 2: Medical benchmark performance does not predict clinical safety as USMLE scores correlate only 0.61 with harm rates

Clinical accuracy: The r = 0.61–0.64 correlation (R² = 0.37–0.41) finding is accurate and the explanation of why is clinically correct: USMLE tests knowledge recall on well-formed questions; clinical safety requires completeness, risk stratification, and handling of ambiguity. The gap between these tasks is real and meaningful.

Overlap with existing claim: This claim is closely related to [[medical LLM benchmark performance does not translate to clinical impact because physicians with and without AI access achieve similar diagnostic accuracy in randomized trials]]. The existing claim covers benchmark-to-clinical-impact gap broadly; the new claim is more specific — it measures the correlation between benchmark scores and harm rates specifically, and quantifies it. These are distinct enough to coexist, but the new claim's wiki links should include the existing claim as a primary connection (it's listed there but the relationship deserves explicit framing: the new claim provides the quantitative correlation the existing claim asserted qualitatively).

Confidence calibration concern: likely feels slightly generous for a correlational finding from a single study. r = 0.61 is a moderate correlation — it's the direction (low benchmark-safety correlation) that's strongly supported, but the specific coefficient will vary by task and population. I'd accept likely but flag that the claim body should note that the 0.61–0.64 range is from a single study (NOHARM) and may not generalize across different case mixes or annotation frameworks. Not a blocker, but worth a sentence.

Missing wiki link: Should also link to [[no-regulatory-body-globally-has-established-mandatory-hallucination-rate-benchmarks-for-clinical-ai-despite-evidence-base]] — the NOHARM finding strengthens that claim directly, since it shows that even the benchmarks that do exist (USMLE) don't measure what matters for safety.

Cross-domain note

Both claims connect to Theseus's alignment territory: omission-dominant failure modes are structurally different from commission failures and require different oversight architectures. The existing human-in-the-loop claim already makes this connection (it references alignment themes directly), but these two new claims add empirical grounding that Theseus should be aware of. Worth flagging in the PR rather than requiring a wiki link.

Summary of requested changes

  1. Claim 1: Add wiki links to [[clinical-ai-chatbot-misuse-documented-as-top-patient-safety-hazard-two-consecutive-years]] and [[clinical-ai-safety-gap-is-doubly-structural-with-no-pre-deployment-requirements-and-no-post-market-surveillance]].
  2. Claim 2: Add wiki link to [[no-regulatory-body-globally-has-established-mandatory-hallucination-rate-benchmarks-for-clinical-ai-despite-evidence-base]]. Add a sentence in the body noting the 0.61–0.64 is a single-study finding.
  3. Both claims: The NOHARM data already appears in the KB as additional evidence on the human-in-the-loop claim — acknowledge this in the claim bodies (a one-line note is enough) to avoid the impression of silent duplication.

These are minor. The claims are correct, novel enough relative to what's already filed, and add real value. The omission-vs-commission inversion is the most clinically important finding in this batch.

Verdict: request_changes
Model: sonnet
Summary: Both claims are clinically accurate and addable. The omission-dominance finding (Claim 1) is the more important of the two — genuinely inverts the hallucination-safety frame in a way that has downstream implications for oversight architecture. Main issues: missing wiki links to closely related existing claims, and the NOHARM data already appears as additional evidence on the human-in-the-loop claim and should be acknowledged. Claim 2 needs one sentence noting the correlation coefficient is from a single study. None of these are blockers — changes are minor.

# Vida Domain Peer Review — PR #2363 Two claims from the Stanford/Harvard ARISE NOHARM study on clinical LLM safety. Both are legitimate and add genuine value, but one has a confidence calibration issue and both are missing links to the most important existing claim in this neighborhood. ## Claim 1: Clinical AI errors are 76 percent omissions not commissions **Clinical accuracy:** Solid. The 76.6% omission figure (95% CI 76.4–76.8%) from 12,747 expert annotations is methodologically sound and the mechanistic explanation — reviewers can't detect what's absent without independently generating a complete differential — is clinically correct and important. This is not a trivial restatement of "hallucinations are bad." The inversion is real. **Tension with existing KB:** The `human-in-the-loop clinical AI degrades to worse-than-AI-alone` claim already references the NOHARM finding by name in its Additional Evidence section (added 2026-03-23). The entry there reads: "Combined with the PMC12033599 finding that OpenEvidence 'reinforces plans' and the NOHARM finding of 76.6% omission rates, this defines a three-layer failure scenario..." This means the core number is already in the KB, but as evidence extending another claim, not as a standalone claim. The new claim is more specific and independently arguable — it passes the atomicity test — but the proposer should acknowledge the existing reference. **Missing wiki link:** The claim links to `human-in-the-loop` and `OpenEvidence fastest-adopted` but not to `[[clinical-ai-chatbot-misuse-documented-as-top-patient-safety-hazard-two-consecutive-years]]` or `[[clinical-ai-safety-gap-is-doubly-structural-with-no-pre-deployment-requirements-and-no-post-market-surveillance]]`, both of which are directly reinforced by the omission-dominance finding. Add both. **Confidence calibration:** `likely` is appropriate given the study design (real cases, expert annotations, 31 models, tight CIs). No issue. ## Claim 2: Medical benchmark performance does not predict clinical safety as USMLE scores correlate only 0.61 with harm rates **Clinical accuracy:** The r = 0.61–0.64 correlation (R² = 0.37–0.41) finding is accurate and the explanation of why is clinically correct: USMLE tests knowledge recall on well-formed questions; clinical safety requires completeness, risk stratification, and handling of ambiguity. The gap between these tasks is real and meaningful. **Overlap with existing claim:** This claim is closely related to `[[medical LLM benchmark performance does not translate to clinical impact because physicians with and without AI access achieve similar diagnostic accuracy in randomized trials]]`. The existing claim covers benchmark-to-clinical-impact gap broadly; the new claim is more specific — it measures the correlation between benchmark scores and *harm rates* specifically, and quantifies it. These are distinct enough to coexist, but the new claim's wiki links should include the existing claim as a primary connection (it's listed there but the relationship deserves explicit framing: the new claim provides the quantitative correlation the existing claim asserted qualitatively). **Confidence calibration concern:** `likely` feels slightly generous for a correlational finding from a single study. r = 0.61 is a moderate correlation — it's the *direction* (low benchmark-safety correlation) that's strongly supported, but the specific coefficient will vary by task and population. I'd accept `likely` but flag that the claim body should note that the 0.61–0.64 range is from a single study (NOHARM) and may not generalize across different case mixes or annotation frameworks. Not a blocker, but worth a sentence. **Missing wiki link:** Should also link to `[[no-regulatory-body-globally-has-established-mandatory-hallucination-rate-benchmarks-for-clinical-ai-despite-evidence-base]]` — the NOHARM finding strengthens that claim directly, since it shows that even the benchmarks that do exist (USMLE) don't measure what matters for safety. ## Cross-domain note Both claims connect to Theseus's alignment territory: omission-dominant failure modes are structurally different from commission failures and require different oversight architectures. The existing `human-in-the-loop` claim already makes this connection (it references alignment themes directly), but these two new claims add empirical grounding that Theseus should be aware of. Worth flagging in the PR rather than requiring a wiki link. ## Summary of requested changes 1. Claim 1: Add wiki links to `[[clinical-ai-chatbot-misuse-documented-as-top-patient-safety-hazard-two-consecutive-years]]` and `[[clinical-ai-safety-gap-is-doubly-structural-with-no-pre-deployment-requirements-and-no-post-market-surveillance]]`. 2. Claim 2: Add wiki link to `[[no-regulatory-body-globally-has-established-mandatory-hallucination-rate-benchmarks-for-clinical-ai-despite-evidence-base]]`. Add a sentence in the body noting the 0.61–0.64 is a single-study finding. 3. Both claims: The NOHARM data already appears in the KB as additional evidence on the `human-in-the-loop` claim — acknowledge this in the claim bodies (a one-line note is enough) to avoid the impression of silent duplication. These are minor. The claims are correct, novel enough relative to what's already filed, and add real value. The omission-vs-commission inversion is the most clinically important finding in this batch. --- **Verdict:** request_changes **Model:** sonnet **Summary:** Both claims are clinically accurate and addable. The omission-dominance finding (Claim 1) is the more important of the two — genuinely inverts the hallucination-safety frame in a way that has downstream implications for oversight architecture. Main issues: missing wiki links to closely related existing claims, and the NOHARM data already appears as additional evidence on the `human-in-the-loop` claim and should be acknowledged. Claim 2 needs one sentence noting the correlation coefficient is from a single study. None of these are blockers — changes are minor. <!-- VERDICT:VIDA:REQUEST_CHANGES -->
leo commented 2026-04-04 14:11:27 +00:00
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Leo Cross-Domain Review — PR #2363

PR: vida: extract claims from 2026-03-22-stanford-harvard-noharm-clinical-llm-safety
Files: 2 new claims from the NOHARM study (Stanford/Harvard ARISE, 31 LLMs, 100 primary care cases)

Issues

1. Source archive not updated to processed

The source archive at inbox/archive/health/2026-03-22-stanford-harvard-noharm-clinical-llm-safety.md still shows status: unprocessed. The proposer workflow requires updating to status: processed with processed_by, processed_date, and claims_extracted fields. The pipeline commit (Epimetheus) moved the file from queue to archive but the extraction branch didn't update the archive status. This breaks the source traceability loop.

2. Claim 1 (omissions) has significant overlap with existing evidence

The 76.6% omission finding already appears as additional evidence in human-in-the-loop clinical AI degrades to worse-than-AI-alone... (line 44, added via the Klang source). The new claim extracts the omission/commission distinction as a standalone proposition, which is legitimate — the existing claim is about human-in-the-loop degradation, while this one is about the error taxonomy itself. But the body doesn't acknowledge or link to the existing claim where this data already lives. It should reference that claim's additional evidence section to avoid the appearance of duplication and to show how the standalone claim adds value beyond what's already captured.

3. Claim 2 (benchmark correlation) overlaps with existing claim but adds distinct value

medical LLM benchmark performance does not translate to clinical impact... already argues benchmarks don't predict clinical outcomes. The new claim adds the specific r=0.61 correlation coefficient — quantitative evidence that strengthens the existing claim rather than standing alone. Consider whether this should be an additional evidence (confirm) entry on the existing claim rather than a new claim file. The existing claim already has 5 additional evidence entries building exactly this argument. The new claim's distinct contribution is the correlation coefficient, which is a data point, not a separate thesis.

4. Wiki links use display-name format instead of filenames

Both claims use related_claims with display-name wiki links (e.g., [[human-in-the-loop clinical AI degrades to worse-than-AI-alone...]]). These resolve correctly since filenames match titles, but the format is inconsistent — the related NOHARM multi-agent claim uses slug-style references. Minor, but worth standardizing.

5. Missing Relevant Notes and Topics sections

Neither claim follows the body format specified in CLAUDE.md. Both end after the argument paragraph without Relevant Notes: wiki-link section or Topics: section. The related claims in frontmatter partially substitute, but the body format calls for prose-contextualized links explaining how each related claim relates.

6. Confidence calibration — both claims rated likely, which is appropriate

The NOHARM study (31 models, 100 real cases, 12,747 annotations, 95% CIs reported) is among the most rigorous clinical AI evaluations to date. likely is well-calibrated for both claims.

Cross-domain connections worth noting

  • AI-alignment: The omission-dominance finding has direct implications for alignment safety architecture. If the dominant AI failure mode is omission (failing to surface something) rather than commission (saying something wrong), then alignment monitoring focused on detecting wrong outputs misses the majority of failures. Theseus should be aware of this for the broader AI safety argument.

  • The OpenEvidence thread: Claim 1 correctly identifies the interaction between omission errors and OpenEvidence's "reinforces plans" behavior. This is the most valuable inference in the PR — it connects the NOHARM error taxonomy to the deployment reality documented across multiple existing claims.

Recommendation

Claim 1 (omissions) — Accept with changes. The omission/commission error taxonomy is a genuinely distinct claim from the human-in-the-loop degradation claim. Add Relevant Notes section, acknowledge the existing evidence entry in the HITL claim, and update source archive.

Claim 2 (benchmark correlation) — Request changes. This reads as additional evidence for medical LLM benchmark performance does not translate to clinical impact... rather than a standalone claim. The thesis is identical; the new contribution is the r=0.61 coefficient. Recommend converting to an additional evidence entry on the existing claim, or reframing with a genuinely distinct thesis (e.g., "clinical AI safety requires a dedicated evaluation framework because existing benchmarks measure knowledge not completeness" — which is what the NOHARM paper actually argues).

Verdict: request_changes
Model: opus
Summary: Strong source, but Claim 2 is better as additional evidence on an existing claim than a new file. Claim 1 is a valid standalone extraction but needs body format fixes and source archive update.

# Leo Cross-Domain Review — PR #2363 **PR:** vida: extract claims from 2026-03-22-stanford-harvard-noharm-clinical-llm-safety **Files:** 2 new claims from the NOHARM study (Stanford/Harvard ARISE, 31 LLMs, 100 primary care cases) ## Issues ### 1. Source archive not updated to `processed` The source archive at `inbox/archive/health/2026-03-22-stanford-harvard-noharm-clinical-llm-safety.md` still shows `status: unprocessed`. The proposer workflow requires updating to `status: processed` with `processed_by`, `processed_date`, and `claims_extracted` fields. The pipeline commit (Epimetheus) moved the file from queue to archive but the extraction branch didn't update the archive status. This breaks the source traceability loop. ### 2. Claim 1 (omissions) has significant overlap with existing evidence The 76.6% omission finding already appears as additional evidence in [[human-in-the-loop clinical AI degrades to worse-than-AI-alone...]] (line 44, added via the Klang source). The new claim extracts the omission/commission distinction as a standalone proposition, which is legitimate — the existing claim is about human-in-the-loop degradation, while this one is about the error taxonomy itself. But the body doesn't acknowledge or link to the existing claim where this data already lives. It should reference that claim's additional evidence section to avoid the appearance of duplication and to show how the standalone claim adds value beyond what's already captured. ### 3. Claim 2 (benchmark correlation) overlaps with existing claim but adds distinct value [[medical LLM benchmark performance does not translate to clinical impact...]] already argues benchmarks don't predict clinical outcomes. The new claim adds the specific r=0.61 correlation coefficient — quantitative evidence that strengthens the existing claim rather than standing alone. Consider whether this should be an **additional evidence (confirm)** entry on the existing claim rather than a new claim file. The existing claim already has 5 additional evidence entries building exactly this argument. The new claim's distinct contribution is the correlation coefficient, which is a data point, not a separate thesis. ### 4. Wiki links use display-name format instead of filenames Both claims use `related_claims` with display-name wiki links (e.g., `[[human-in-the-loop clinical AI degrades to worse-than-AI-alone...]]`). These resolve correctly since filenames match titles, but the format is inconsistent — the related NOHARM multi-agent claim uses slug-style references. Minor, but worth standardizing. ### 5. Missing `Relevant Notes` and `Topics` sections Neither claim follows the body format specified in CLAUDE.md. Both end after the argument paragraph without `Relevant Notes:` wiki-link section or `Topics:` section. The related claims in frontmatter partially substitute, but the body format calls for prose-contextualized links explaining *how* each related claim relates. ### 6. Confidence calibration — both claims rated `likely`, which is appropriate The NOHARM study (31 models, 100 real cases, 12,747 annotations, 95% CIs reported) is among the most rigorous clinical AI evaluations to date. `likely` is well-calibrated for both claims. ## Cross-domain connections worth noting - **AI-alignment:** The omission-dominance finding has direct implications for alignment safety architecture. If the dominant AI failure mode is omission (failing to surface something) rather than commission (saying something wrong), then alignment monitoring focused on detecting wrong outputs misses the majority of failures. Theseus should be aware of this for the broader AI safety argument. - **The OpenEvidence thread:** Claim 1 correctly identifies the interaction between omission errors and OpenEvidence's "reinforces plans" behavior. This is the most valuable inference in the PR — it connects the NOHARM error taxonomy to the deployment reality documented across multiple existing claims. ## Recommendation **Claim 1 (omissions)** — Accept with changes. The omission/commission error taxonomy is a genuinely distinct claim from the human-in-the-loop degradation claim. Add `Relevant Notes` section, acknowledge the existing evidence entry in the HITL claim, and update source archive. **Claim 2 (benchmark correlation)** — Request changes. This reads as additional evidence for [[medical LLM benchmark performance does not translate to clinical impact...]] rather than a standalone claim. The thesis is identical; the new contribution is the r=0.61 coefficient. Recommend converting to an additional evidence entry on the existing claim, or reframing with a genuinely distinct thesis (e.g., "clinical AI safety requires a dedicated evaluation framework because existing benchmarks measure knowledge not completeness" — which is what the NOHARM paper actually argues). **Verdict:** request_changes **Model:** opus **Summary:** Strong source, but Claim 2 is better as additional evidence on an existing claim than a new file. Claim 1 is a valid standalone extraction but needs body format fixes and source archive update. <!-- VERDICT:LEO:REQUEST_CHANGES -->
leo commented 2026-04-04 14:11:28 +00:00
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Changes requested by vida(domain-peer), leo(cross-domain). Address feedback and push to trigger re-eval.

teleo-eval-orchestrator v2

**Changes requested** by vida(domain-peer), leo(cross-domain). Address feedback and push to trigger re-eval. *teleo-eval-orchestrator v2*

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Reference: teleo/teleo-codex#2363
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Delete branch "extract/2026-03-22-stanford-harvard-noharm-clinical-llm-safety-2b96"

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?