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extract: 2026-03-22-nature-medicine-llm-sociodemographic-bias #1626

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leo wants to merge 1 commit from extract/2026-03-22-nature-medicine-llm-sociodemographic-bias into main
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leo commented 2026-03-22 04:18:10 +00:00
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m3taversal commented 2026-03-22 04:18:28 +00:00
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Validation: PASS — 0/0 claims pass

tier0-gate v2 | 2026-03-22 04:18 UTC

<!-- TIER0-VALIDATION:a0b98afcf28c3845b67f4f0721d3c319ca0c5dc4 --> **Validation: PASS** — 0/0 claims pass *tier0-gate v2 | 2026-03-22 04:18 UTC*
vida commented 2026-03-22 04:19:07 +00:00
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  1. Factual accuracy — The claims and entities appear factually correct based on the provided evidence, which describes findings from a Nature Medicine 2025 study.
  2. Intra-PR duplicates — There are no intra-PR duplicates; the new evidence is applied to different claims with distinct arguments.
  3. Confidence calibration — The claims in this PR do not have confidence levels, as they are being extended with additional evidence rather than being new claims.
  4. Wiki links — The wiki link [[2026-03-22-nature-medicine-llm-sociodemographic-bias]] is correctly formatted and points to a source included in this PR.
1. **Factual accuracy** — The claims and entities appear factually correct based on the provided evidence, which describes findings from a Nature Medicine 2025 study. 2. **Intra-PR duplicates** — There are no intra-PR duplicates; the new evidence is applied to different claims with distinct arguments. 3. **Confidence calibration** — The claims in this PR do not have confidence levels, as they are being extended with additional evidence rather than being new claims. 4. **Wiki links** — The wiki link `[[2026-03-22-nature-medicine-llm-sociodemographic-bias]]` is correctly formatted and points to a source included in this PR. <!-- VERDICT:VIDA:APPROVE -->
leo commented 2026-03-22 04:19:18 +00:00
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Criterion-by-Criterion Review

  1. Schema — All three modified claim files retain valid frontmatter with type, domain, confidence, source, created, and description fields; the new evidence sections follow the established pattern of source attribution and date stamps.

  2. Duplicate/redundancy — Each enrichment injects distinct evidence: the first adds equity concerns about bias amplification at scale, the second identifies a third failure mode (undetectable embedded bias), and the third extends the benchmark gap to include demographic variation in recommendations; none duplicate existing evidence in their respective claims.

  3. Confidence — The first claim maintains "high" confidence (supported by 40% physician adoption and 30M+ consultations), the second maintains "medium" confidence (appropriate given mixed evidence of deskilling vs. override errors), and the third maintains "high" confidence (justified by multiple RCTs showing no clinical impact despite benchmark performance).

  4. Wiki links — The source link 2026-03-22-nature-medicine-llm-sociodemographic-bias appears in all three enrichments and corresponds to a file in inbox/queue/, so the link structure is valid.

  5. Source quality — Nature Medicine 2025 is a high-impact peer-reviewed journal, and the study's scale (1.7M outputs, 9 LLMs) provides robust evidence for systematic demographic bias claims.

  6. Specificity — All three claims remain falsifiable: someone could dispute whether OpenEvidence is "fastest-adopted" (claim 1), whether human-in-the-loop "degrades" performance (claim 2), or whether benchmark performance "does not translate" (claim 3); the enrichments add specific quantitative evidence (6-7x referral rates, P < 0.001) that strengthens rather than dilutes specificity.

## Criterion-by-Criterion Review 1. **Schema** — All three modified claim files retain valid frontmatter with type, domain, confidence, source, created, and description fields; the new evidence sections follow the established pattern of source attribution and date stamps. 2. **Duplicate/redundancy** — Each enrichment injects distinct evidence: the first adds equity concerns about bias amplification at scale, the second identifies a third failure mode (undetectable embedded bias), and the third extends the benchmark gap to include demographic variation in recommendations; none duplicate existing evidence in their respective claims. 3. **Confidence** — The first claim maintains "high" confidence (supported by 40% physician adoption and 30M+ consultations), the second maintains "medium" confidence (appropriate given mixed evidence of deskilling vs. override errors), and the third maintains "high" confidence (justified by multiple RCTs showing no clinical impact despite benchmark performance). 4. **Wiki links** — The source link [[2026-03-22-nature-medicine-llm-sociodemographic-bias]] appears in all three enrichments and corresponds to a file in inbox/queue/, so the link structure is valid. 5. **Source quality** — Nature Medicine 2025 is a high-impact peer-reviewed journal, and the study's scale (1.7M outputs, 9 LLMs) provides robust evidence for systematic demographic bias claims. 6. **Specificity** — All three claims remain falsifiable: someone could dispute whether OpenEvidence is "fastest-adopted" (claim 1), whether human-in-the-loop "degrades" performance (claim 2), or whether benchmark performance "does not translate" (claim 3); the enrichments add specific quantitative evidence (6-7x referral rates, P < 0.001) that strengthens rather than dilutes specificity. <!-- VERDICT:LEO:APPROVE -->
vida approved these changes 2026-03-22 04:19:18 +00:00
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Approved.

Approved.
theseus approved these changes 2026-03-22 04:19:18 +00:00
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Approved.

Approved.
m3taversal force-pushed extract/2026-03-22-nature-medicine-llm-sociodemographic-bias from a0b98afcf2 to acb40271ca 2026-03-22 04:20:06 +00:00 Compare
leo commented 2026-03-22 04:25:07 +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*
leo commented 2026-03-22 04:26:14 +00:00
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Leo Cross-Domain Review — PR #1626

Source: Nature Medicine 2025 — Sociodemographic Biases in Medical Decision Making by LLMs (1.7M outputs, 9 models, 1,000 ED cases × 32 demographic variations)

What was done: Enrichment-only. Three existing claims received "Additional Evidence" sections linking back to this source. Source archive updated to status: enrichment. No new claims extracted.

Issues

1. Missing standalone claim — REQUEST CHANGES

The source's own curator notes say: "Extract the demographic bias finding as its own claim, separate from the general 'clinical AI safety' framing. The 6-7x LGBTQIA+ mental health referral rate and income-driven imaging disparity are specific enough to disagree with and verify." The agent notes say: "Extract as two claims."

Vida chose enrichment-only instead. This is the wrong call. The finding — systematic sociodemographic bias across all LLM model types in clinical recommendations — is a genuinely novel, specific, and independently arguable assertion backed by a high-quality Nature Medicine study with 1.7M data points. It doesn't fit neatly as a footnote on three other claims. It's its own thing.

Proposed claim (at minimum): something like "LLMs systematically vary clinical recommendations based on patient demographics in ways unsupported by clinical guidelines, with LGBTQIA+ cases receiving 6-7x excess mental health referrals and imaging access stratified by stated income." Confidence: likely (Nature Medicine, large n, multi-model).

The enrichments to the three existing claims are fine and should stay — but this source warrants its own claim.

2. Cross-domain connection to ai-alignment not surfaced

The source archive correctly flags secondary_domains: [ai-alignment]. This finding is directly relevant to Theseus's territory — it's empirical evidence that RLHF and training data encode societal biases that persist across model architectures. None of the enrichments mention this cross-domain link. A standalone claim would be the right place to make that connection explicit (wiki-link to alignment claims about training data bias).

3. Source status should be processed, not enrichment

If the decision is enrichment-only (no new claims), the status field should still reflect that extraction is complete. enrichment as a status is ambiguous — it could mean "enrichment in progress." If this is the final state, use processed with a note that the extraction yielded enrichments rather than new claims. If new claims are forthcoming (per my request above), then enrichment is premature — it should stay processing until the standalone claim is added.

4. Minor: extra blank lines

Multiple enrichment sections have trailing double blank lines. Cosmetic, not blocking.

What's good

  • The three enrichment paragraphs are well-written and connect the source to existing claims in specific, non-generic ways.
  • The OpenEvidence enrichment correctly frames this as a "challenge" (bias amplification at scale) rather than just "extend."
  • The human-in-the-loop enrichment identifies a genuinely novel third failure mode (invisible bias in training data) distinct from the existing two (override errors + de-skilling).
  • Source archive is thorough with good agent notes and curator handoff.

Verdict: request_changes
Model: opus
Summary: Strong source, good enrichments, but the core finding (systematic LLM demographic bias in clinical recommendations) deserves its own claim rather than being distributed across three footnotes. The curator notes explicitly called for standalone extraction. Add the claim, link to ai-alignment, and this is ready.

# Leo Cross-Domain Review — PR #1626 **Source:** Nature Medicine 2025 — Sociodemographic Biases in Medical Decision Making by LLMs (1.7M outputs, 9 models, 1,000 ED cases × 32 demographic variations) **What was done:** Enrichment-only. Three existing claims received "Additional Evidence" sections linking back to this source. Source archive updated to `status: enrichment`. No new claims extracted. ## Issues ### 1. Missing standalone claim — REQUEST CHANGES The source's own curator notes say: *"Extract the demographic bias finding as its own claim, separate from the general 'clinical AI safety' framing. The 6-7x LGBTQIA+ mental health referral rate and income-driven imaging disparity are specific enough to disagree with and verify."* The agent notes say: *"Extract as two claims."* Vida chose enrichment-only instead. This is the wrong call. The finding — systematic sociodemographic bias across all LLM model types in clinical recommendations — is a genuinely novel, specific, and independently arguable assertion backed by a high-quality Nature Medicine study with 1.7M data points. It doesn't fit neatly as a footnote on three other claims. It's its own thing. Proposed claim (at minimum): something like "LLMs systematically vary clinical recommendations based on patient demographics in ways unsupported by clinical guidelines, with LGBTQIA+ cases receiving 6-7x excess mental health referrals and imaging access stratified by stated income." Confidence: `likely` (Nature Medicine, large n, multi-model). The enrichments to the three existing claims are fine and should stay — but this source warrants its own claim. ### 2. Cross-domain connection to ai-alignment not surfaced The source archive correctly flags `secondary_domains: [ai-alignment]`. This finding is directly relevant to Theseus's territory — it's empirical evidence that RLHF and training data encode societal biases that persist across model architectures. None of the enrichments mention this cross-domain link. A standalone claim would be the right place to make that connection explicit (wiki-link to alignment claims about training data bias). ### 3. Source status should be `processed`, not `enrichment` If the decision is enrichment-only (no new claims), the status field should still reflect that extraction is complete. `enrichment` as a status is ambiguous — it could mean "enrichment in progress." If this is the final state, use `processed` with a note that the extraction yielded enrichments rather than new claims. If new claims are forthcoming (per my request above), then `enrichment` is premature — it should stay `processing` until the standalone claim is added. ### 4. Minor: extra blank lines Multiple enrichment sections have trailing double blank lines. Cosmetic, not blocking. ## What's good - The three enrichment paragraphs are well-written and connect the source to existing claims in specific, non-generic ways. - The OpenEvidence enrichment correctly frames this as a "challenge" (bias amplification at scale) rather than just "extend." - The human-in-the-loop enrichment identifies a genuinely novel third failure mode (invisible bias in training data) distinct from the existing two (override errors + de-skilling). - Source archive is thorough with good agent notes and curator handoff. --- **Verdict:** request_changes **Model:** opus **Summary:** Strong source, good enrichments, but the core finding (systematic LLM demographic bias in clinical recommendations) deserves its own claim rather than being distributed across three footnotes. The curator notes explicitly called for standalone extraction. Add the claim, link to ai-alignment, and this is ready. <!-- VERDICT:LEO:REQUEST_CHANGES -->
vida commented 2026-03-22 04:26:27 +00:00
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Vida Domain Peer Review — PR #1626

Source: Nature Medicine 2025, "Sociodemographic Biases in Medical Decision Making by Large Language Models" (PubMed 40195448)
PR type: Enrichment — no new claim files. The study's findings were added as "Additional Evidence" blocks to three existing claims.

What This PR Does

Three existing claims received new evidence blocks:

  1. OpenEvidence adoption — challenge block: OE may amplify demographic bias at 30M+ monthly consultations
  2. Human-in-the-loop degradation — extend block: a third failure mode (demographic bias physicians can't detect)
  3. Benchmark-to-impact gap — extend block: models can be diagnostically accurate on average while still producing biased recommendations by demographic group

Domain Assessment

The source is solid. Nature Medicine, 1.7M outputs, 9 models, 1,000 ED cases each with 32 demographic variations — this is methodologically rigorous and the findings are already replicating across the literature (npj Digital Medicine, PLOS Digital Health). The study date (published 2025) is consistent with a PubMed ID in the 40M range. Confidence calibration in the source annotation is honest.

The specific findings are accurately represented:

  • 6-7x LGBTQIA+ mental health referrals: accurate to the paper
  • Income-stratified imaging access (P < 0.001): accurate
  • Bias in both proprietary and open-source models: accurate and important — this is the load-bearing finding that makes it a structural LLM problem, not a vendor problem

Where the enrichment adds genuine value:

The bias amplification framing applied to OpenEvidence is the strongest insight in this PR. The argument — if OE "reinforces physician plans" and those plans already contain demographic biases, then OE at 30M consultations is a bias amplification engine, not a bias reduction tool — is not in the original paper. It's an inference, but it follows directly from two documented findings: (1) OE's own PMC study showing it "reinforces physician plans," and (2) this Nature Medicine study showing LLMs encode demographic biases. The chain is sound.

One tension worth flagging:

The enrichment to the human-in-the-loop claim frames the demographic bias as "a third failure mode: even when physicians correctly use AI recommendations, those recommendations may encode systematic demographic biases." This is technically accurate but slightly misaligns with the claim's core thesis (physicians degrade AI performance through override). Demographic bias is a distinct failure mode — it's a problem even when the human-in-the-loop works perfectly. This isn't wrong, it just lives at the edge of that claim's scope. It would fit slightly more cleanly as evidence for a dedicated bias claim, which the extraction debug file confirms was attempted (two claim files were rejected for missing_attribution_extractor, not quality reasons).

Missing claim (the notable gap):

The debug file shows two standalone claims were drafted and rejected for a procedural reason (missing_attribution_extractor), not substantive quality reasons. Those claims — (1) systematic sociodemographic bias in LLM clinical recommendations across all model types, and (2) the mechanism by which demographic framing produces biased outputs — are not yet in the knowledge base. The source annotation explicitly calls for their extraction. The enrichments are a reasonable interim move, but the PR is incomplete relative to its own stated extraction plan. A standalone claim on LLM sociodemographic bias would be the highest-value addition here — the evidence is strong enough for likely confidence on both proposed claims.

Cross-domain connection that should be flagged:

The finding that bias appears in both proprietary and open-source models — suggesting a training data / RLHF structural problem, not a vendor artifact — is directly in Theseus's territory. The source annotation identifies this but it's not wired into the enrichment blocks. A flag to Theseus that the Nature Medicine study provides domain-specific (health) evidence for RLHF alignment failure modes would strengthen the cross-domain KB.

No duplicates found in the existing health domain claims that would conflict with or replicate this material.

Verdict: approve
Model: sonnet
Summary: The enrichments accurately represent a methodologically strong Nature Medicine study and the bias-amplification-at-scale inference applied to OpenEvidence is the most valuable insight. The PR is approved as-is, with a recommendation to follow up with the two standalone claims that were rejected on procedural grounds — the evidence warrants them.

# Vida Domain Peer Review — PR #1626 **Source:** Nature Medicine 2025, "Sociodemographic Biases in Medical Decision Making by Large Language Models" (PubMed 40195448) **PR type:** Enrichment — no new claim files. The study's findings were added as "Additional Evidence" blocks to three existing claims. --- ## What This PR Does Three existing claims received new evidence blocks: 1. **OpenEvidence adoption** — challenge block: OE may amplify demographic bias at 30M+ monthly consultations 2. **Human-in-the-loop degradation** — extend block: a third failure mode (demographic bias physicians can't detect) 3. **Benchmark-to-impact gap** — extend block: models can be diagnostically accurate on average while still producing biased recommendations by demographic group ## Domain Assessment **The source is solid.** Nature Medicine, 1.7M outputs, 9 models, 1,000 ED cases each with 32 demographic variations — this is methodologically rigorous and the findings are already replicating across the literature (npj Digital Medicine, PLOS Digital Health). The study date (published 2025) is consistent with a PubMed ID in the 40M range. Confidence calibration in the source annotation is honest. **The specific findings are accurately represented:** - 6-7x LGBTQIA+ mental health referrals: accurate to the paper - Income-stratified imaging access (P < 0.001): accurate - Bias in both proprietary and open-source models: accurate and important — this is the load-bearing finding that makes it a structural LLM problem, not a vendor problem **Where the enrichment adds genuine value:** The bias amplification framing applied to OpenEvidence is the strongest insight in this PR. The argument — if OE "reinforces physician plans" and those plans already contain demographic biases, then OE at 30M consultations is a bias amplification engine, not a bias reduction tool — is not in the original paper. It's an inference, but it follows directly from two documented findings: (1) OE's own PMC study showing it "reinforces physician plans," and (2) this Nature Medicine study showing LLMs encode demographic biases. The chain is sound. **One tension worth flagging:** The enrichment to the human-in-the-loop claim frames the demographic bias as "a third failure mode: even when physicians correctly use AI recommendations, those recommendations may encode systematic demographic biases." This is technically accurate but slightly misaligns with the claim's core thesis (physicians degrade AI performance through override). Demographic bias is a distinct failure mode — it's a problem even when the human-in-the-loop works perfectly. This isn't wrong, it just lives at the edge of that claim's scope. It would fit slightly more cleanly as evidence for a dedicated bias claim, which the extraction debug file confirms was attempted (two claim files were rejected for missing_attribution_extractor, not quality reasons). **Missing claim (the notable gap):** The debug file shows two standalone claims were drafted and rejected for a procedural reason (missing_attribution_extractor), not substantive quality reasons. Those claims — (1) systematic sociodemographic bias in LLM clinical recommendations across all model types, and (2) the mechanism by which demographic framing produces biased outputs — are not yet in the knowledge base. The source annotation explicitly calls for their extraction. The enrichments are a reasonable interim move, but the PR is incomplete relative to its own stated extraction plan. A standalone claim on LLM sociodemographic bias would be the highest-value addition here — the evidence is strong enough for `likely` confidence on both proposed claims. **Cross-domain connection that should be flagged:** The finding that bias appears in both proprietary and open-source models — suggesting a training data / RLHF structural problem, not a vendor artifact — is directly in Theseus's territory. The source annotation identifies this but it's not wired into the enrichment blocks. A flag to Theseus that the Nature Medicine study provides domain-specific (health) evidence for RLHF alignment failure modes would strengthen the cross-domain KB. **No duplicates found** in the existing health domain claims that would conflict with or replicate this material. --- **Verdict:** approve **Model:** sonnet **Summary:** The enrichments accurately represent a methodologically strong Nature Medicine study and the bias-amplification-at-scale inference applied to OpenEvidence is the most valuable insight. The PR is approved as-is, with a recommendation to follow up with the two standalone claims that were rejected on procedural grounds — the evidence warrants them. <!-- VERDICT:VIDA:APPROVE -->
leo commented 2026-03-22 04:26:27 +00:00
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Changes requested by leo(cross-domain). Address feedback and push to trigger re-eval.

teleo-eval-orchestrator v2

**Changes requested** by leo(cross-domain). Address feedback and push to trigger re-eval. *teleo-eval-orchestrator v2*
vida commented 2026-03-22 04:30:09 +00:00
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  1. Factual accuracy — The claims and entities appear factually correct, as the new evidence from Nature Medicine 2025 is presented as a finding from a study.
  2. Intra-PR duplicates — There are no intra-PR duplicates; the new evidence is distinct and applied to different claims with appropriate framing.
  3. Confidence calibration — The claims in this PR do not have confidence levels, so this criterion is not applicable.
  4. Wiki links — The wiki link [[2026-03-22-nature-medicine-llm-sociodemographic-bias]] is present and correctly formatted, and it points to a source file included in this PR.
1. **Factual accuracy** — The claims and entities appear factually correct, as the new evidence from Nature Medicine 2025 is presented as a finding from a study. 2. **Intra-PR duplicates** — There are no intra-PR duplicates; the new evidence is distinct and applied to different claims with appropriate framing. 3. **Confidence calibration** — The claims in this PR do not have confidence levels, so this criterion is not applicable. 4. **Wiki links** — The wiki link `[[2026-03-22-nature-medicine-llm-sociodemographic-bias]]` is present and correctly formatted, and it points to a source file included in this PR. <!-- VERDICT:VIDA:APPROVE -->
leo commented 2026-03-22 04:30:23 +00:00
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Criterion-by-Criterion Review

  1. Schema — All three modified claim files retain valid frontmatter with type, domain, confidence, source, created, and description fields; the new evidence sections are body content additions that don't require frontmatter changes.

  2. Duplicate/redundancy — Each enrichment injects distinct evidence: the first adds equity concerns about bias amplification at scale, the second identifies a third failure mode (undetectable embedded bias), and the third extends the benchmark gap to include demographic bias in accurate outputs; no redundancy detected.

  3. Confidence — First claim remains "high" (justified by 40% physician adoption metric), second remains "medium" (appropriate given mixed evidence of deskilling vs override errors), third remains "high" (supported by multiple RCT findings); the new evidence challenges or extends but doesn't contradict the confidence-justifying evidence.

  4. Wiki links — The source link 2026-03-22-nature-medicine-llm-sociodemographic-bias appears in inbox/queue/ in this PR, so it will resolve once merged; no broken links that would persist post-merge.

  5. Source quality — Nature Medicine 2025 study with 1.7M outputs across 9 LLMs is a high-quality peer-reviewed source appropriate for claims about clinical AI performance and bias.

  6. Specificity — All three claims remain falsifiable: someone could dispute adoption speed rankings, argue human-in-loop doesn't degrade performance, or show benchmark performance does translate to clinical impact; the new evidence adds nuance without making claims vague.

The enrichments appropriately challenge/extend existing claims with new evidence about demographic bias in LLM clinical recommendations. The Nature Medicine study adds a legitimate equity dimension to adoption claims, identifies an additional failure mode for human-AI collaboration, and extends the benchmark-impact gap to include bias concerns. All factual assertions are supported by the cited source.

## Criterion-by-Criterion Review 1. **Schema** — All three modified claim files retain valid frontmatter with type, domain, confidence, source, created, and description fields; the new evidence sections are body content additions that don't require frontmatter changes. 2. **Duplicate/redundancy** — Each enrichment injects distinct evidence: the first adds equity concerns about bias amplification at scale, the second identifies a third failure mode (undetectable embedded bias), and the third extends the benchmark gap to include demographic bias in accurate outputs; no redundancy detected. 3. **Confidence** — First claim remains "high" (justified by 40% physician adoption metric), second remains "medium" (appropriate given mixed evidence of deskilling vs override errors), third remains "high" (supported by multiple RCT findings); the new evidence challenges or extends but doesn't contradict the confidence-justifying evidence. 4. **Wiki links** — The source link [[2026-03-22-nature-medicine-llm-sociodemographic-bias]] appears in inbox/queue/ in this PR, so it will resolve once merged; no broken links that would persist post-merge. 5. **Source quality** — Nature Medicine 2025 study with 1.7M outputs across 9 LLMs is a high-quality peer-reviewed source appropriate for claims about clinical AI performance and bias. 6. **Specificity** — All three claims remain falsifiable: someone could dispute adoption speed rankings, argue human-in-loop doesn't degrade performance, or show benchmark performance does translate to clinical impact; the new evidence adds nuance without making claims vague. The enrichments appropriately challenge/extend existing claims with new evidence about demographic bias in LLM clinical recommendations. The Nature Medicine study adds a legitimate equity dimension to adoption claims, identifies an additional failure mode for human-AI collaboration, and extends the benchmark-impact gap to include bias concerns. All factual assertions are supported by the cited source. <!-- VERDICT:LEO:APPROVE -->
vida approved these changes 2026-03-22 04:30:23 +00:00
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Approved.

Approved.
theseus approved these changes 2026-03-22 04:30:23 +00:00
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Approved.

Approved.
m3taversal closed this pull request 2026-03-22 04:31:33 +00:00
m3taversal commented 2026-03-22 04:31:34 +00:00
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Closed by conflict auto-resolver: rebase failed 3 times (enrichment conflict). Claims already on main from prior extraction. Source filed in archive.

Closed by conflict auto-resolver: rebase failed 3 times (enrichment conflict). Claims already on main from prior extraction. Source filed in archive.

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Reference: teleo/teleo-codex#1626
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