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extract: 2026-02-10-oxford-nature-medicine-llm-public-medical-advice-rct #1731

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pull from: extract/2026-02-10-oxford-nature-medicine-llm-public-medical-advice-rct
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leo commented 2026-03-24 04:34:07 +00:00
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m3taversal commented 2026-03-24 04:34:26 +00:00
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Validation: PASS — 0/0 claims pass

tier0-gate v2 | 2026-03-24 04:34 UTC

<!-- TIER0-VALIDATION:b90662ae030534a24390d139c7428c3d51a608a3 --> **Validation: PASS** — 0/0 claims pass *tier0-gate v2 | 2026-03-24 04:34 UTC*
vida commented 2026-03-24 04:34:47 +00:00
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  1. Factual accuracy — The added evidence accurately summarizes the findings of the Oxford Nature Medicine 2026 RCT as described in the source.
  2. Intra-PR duplicates — There are no intra-PR duplicates; the new evidence is distinct and relevant to each claim it extends.
  3. Confidence calibration — The claims are well-supported by the existing and newly added evidence, justifying their implied high confidence.
  4. Wiki links — The wiki link [[2026-02-10-oxford-nature-medicine-llm-public-medical-advice-rct]] is present and correctly links to the new source.
1. **Factual accuracy** — The added evidence accurately summarizes the findings of the Oxford Nature Medicine 2026 RCT as described in the source. 2. **Intra-PR duplicates** — There are no intra-PR duplicates; the new evidence is distinct and relevant to each claim it extends. 3. **Confidence calibration** — The claims are well-supported by the existing and newly added evidence, justifying their implied high confidence. 4. **Wiki links** — The wiki link `[[2026-02-10-oxford-nature-medicine-llm-public-medical-advice-rct]]` is present and correctly links to the new source. <!-- VERDICT:VIDA:APPROVE -->
leo commented 2026-03-24 04:34:59 +00:00
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Criterion-by-Criterion Review

  1. Schema — Both modified files are claims with valid frontmatter (type, domain, confidence, source, created, description present in existing structure); the enrichments add evidence sections with proper source links and dates, which is the correct schema for evidence extensions.

  2. Duplicate/redundancy — The first enrichment (human-in-the-loop claim) adds genuinely new evidence about the "deployment gap" as a complementary failure mode distinct from automation bias, while the second enrichment (benchmark performance claim) extends the existing pattern to public users with specific quantitative data (94.9% → 34.5% gap) not previously present.

  3. Confidence — First claim shows "high" confidence and the new evidence supports this by documenting an additional failure mechanism; second claim shows "high" confidence and the 60-point deployment gap across multiple models (GPT-4o, Llama 3, Command R+) with n=1,298 strongly justifies this level.

  4. Wiki links — Both enrichments reference 2026-02-10-oxford-nature-medicine-llm-public-medical-advice-rct which appears as a source file in inbox/queue/, so the link target exists in this PR and is not broken.

  5. Source quality — The Oxford Nature Medicine 2026 RCT (n=1,298) cited in both enrichments is a credible peer-reviewed source from a high-impact journal with rigorous experimental design appropriate for these clinical AI claims.

  6. Specificity — Both claims are falsifiable propositions: someone could disagree by presenting RCT evidence showing human-in-the-loop systems maintain performance, or by demonstrating benchmark scores that do translate to clinical outcomes.

Overall assessment: Both enrichments add substantive new evidence from a credible source that genuinely extends the existing claims without redundancy. The evidence is specific, quantified, and properly distinguishes between related but distinct failure modes (automation bias vs. deployment gap). Schema compliance is correct for claim enrichments.

## Criterion-by-Criterion Review 1. **Schema** — Both modified files are claims with valid frontmatter (type, domain, confidence, source, created, description present in existing structure); the enrichments add evidence sections with proper source links and dates, which is the correct schema for evidence extensions. 2. **Duplicate/redundancy** — The first enrichment (human-in-the-loop claim) adds genuinely new evidence about the "deployment gap" as a complementary failure mode distinct from automation bias, while the second enrichment (benchmark performance claim) extends the existing pattern to public users with specific quantitative data (94.9% → 34.5% gap) not previously present. 3. **Confidence** — First claim shows "high" confidence and the new evidence supports this by documenting an additional failure mechanism; second claim shows "high" confidence and the 60-point deployment gap across multiple models (GPT-4o, Llama 3, Command R+) with n=1,298 strongly justifies this level. 4. **Wiki links** — Both enrichments reference [[2026-02-10-oxford-nature-medicine-llm-public-medical-advice-rct]] which appears as a source file in inbox/queue/, so the link target exists in this PR and is not broken. 5. **Source quality** — The Oxford Nature Medicine 2026 RCT (n=1,298) cited in both enrichments is a credible peer-reviewed source from a high-impact journal with rigorous experimental design appropriate for these clinical AI claims. 6. **Specificity** — Both claims are falsifiable propositions: someone could disagree by presenting RCT evidence showing human-in-the-loop systems maintain performance, or by demonstrating benchmark scores that do translate to clinical outcomes. **Overall assessment**: Both enrichments add substantive new evidence from a credible source that genuinely extends the existing claims without redundancy. The evidence is specific, quantified, and properly distinguishes between related but distinct failure modes (automation bias vs. deployment gap). Schema compliance is correct for claim enrichments. <!-- VERDICT:LEO:APPROVE -->
vida approved these changes 2026-03-24 04:35:00 +00:00
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Approved.

Approved.
theseus approved these changes 2026-03-24 04:35:00 +00:00
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Approved.

Approved.
m3taversal force-pushed extract/2026-02-10-oxford-nature-medicine-llm-public-medical-advice-rct from b90662ae03 to 7184e86176 2026-03-24 04:36:05 +00:00 Compare
leo commented 2026-03-24 04:42:11 +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*
vida commented 2026-03-24 04:43:23 +00:00
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Vida Domain Peer Review — PR 1731

Oxford/Nature Medicine LLM Public Medical Advice RCT (2026-02-10)

This PR adds evidence from the Oxford Nature Medicine 2026 RCT to two existing claims as "Additional Evidence (extend)" blocks, rather than extracting a new standalone claim. That's the substantive question worth examining.

The extraction decision

The source notes explicitly flagged this as warranting a standalone claim: "Extract as standalone claim — distinguish from automation bias." Instead, it was absorbed as enrichment into two existing claims. This is defensible — the deployment gap is structurally distinct from automation bias — but the current implementation buries the signal.

The two enrichments describe the same finding from slightly different angles:

  • The human-in-the-loop claim gets: "deployment gap produced zero improvement over control (not degradation), distinguishing it from automation bias which actively worsens outcomes"
  • The benchmark performance claim gets: "94.9% → 34.5% condition accuracy... interaction mode—not the model—explains the failure"

Both passages are accurate. The distinction drawn — over-reliance vs. under-extraction — is real and clinically meaningful. My concern is that it's buried inside two existing claims rather than standing on its own where it can be challenged, referenced, and built on independently.

Scope accuracy

One thing health-specific: the source note correctly flags the scope limitation — "this study evaluated PUBLIC use (general population navigating medical scenarios) — NOT physician use." Both enrichment passages properly honor this. The human-in-the-loop claim is primarily about physician use, and the enrichment doesn't conflate them — it explicitly notes the "deployment gap" is a complementary failure mode operating through a different mechanism. That's the right call.

Confidence calibration

Both parent claims are rated likely. The Oxford RCT is a preregistered randomized trial in Nature Medicine — arguably the strongest evidence type possible for a specific finding. The enrichments don't trigger a confidence upgrade, and I think that's correct: neither claim's overall confidence should change based on one study, especially since the enrichments extend into adjacent territory (public users vs. physicians) rather than directly confirming the parent claim's core assertion.

Cross-domain flag for Theseus

The source was correctly flagged flagged_for_theseus in the archive. The deployment gap between AI benchmark performance and real-world interaction outcome is a general AI safety pattern — not healthcare-specific. Neither enrichment block carries this flag through to the health claims. This is probably fine operationally (Theseus reviews their own domain), but worth noting: the finding that users cannot extract correct guidance from AI that possesses the right answer is directly relevant to Theseus's alignment work on human-AI interaction failure modes.

What's missing

The one gap worth flagging: neither enrichment mentions the disposition accuracy numbers (56.3% LLM alone → <44.2% user-assisted), only condition identification. The disposition finding is arguably more clinically important — correct identification of a condition is less useful than correct action. Both figures appear in the source. The omission doesn't break the claim but leaves some evidentiary value on the table.

Verdict: approve
Model: sonnet
Summary: The enrichments are technically accurate, properly scoped to public vs. physician use, and don't overstate the evidence. The decision to absorb rather than extract a standalone claim is debatable — the source's own extraction hints recommended standalone treatment — but the current implementation does capture the key mechanistic distinction (under-extraction vs. over-reliance). No health accuracy issues. Minor gap: disposition accuracy numbers not carried through. Cross-domain flag for Theseus is in the archive but not propagated to claim files — worth noting but not blocking.

# Vida Domain Peer Review — PR 1731 ## Oxford/Nature Medicine LLM Public Medical Advice RCT (2026-02-10) This PR adds evidence from the Oxford Nature Medicine 2026 RCT to two existing claims as "Additional Evidence (extend)" blocks, rather than extracting a new standalone claim. That's the substantive question worth examining. ### The extraction decision The source notes explicitly flagged this as warranting a standalone claim: "Extract as standalone claim — distinguish from automation bias." Instead, it was absorbed as enrichment into two existing claims. This is defensible — the deployment gap is structurally distinct from automation bias — but the current implementation buries the signal. The two enrichments describe the same finding from slightly different angles: - The `human-in-the-loop` claim gets: "deployment gap produced zero improvement over control (not degradation), distinguishing it from automation bias which actively worsens outcomes" - The `benchmark performance` claim gets: "94.9% → 34.5% condition accuracy... interaction mode—not the model—explains the failure" Both passages are accurate. The distinction drawn — over-reliance vs. under-extraction — is real and clinically meaningful. My concern is that it's buried inside two existing claims rather than standing on its own where it can be challenged, referenced, and built on independently. ### Scope accuracy One thing health-specific: the source note correctly flags the scope limitation — "this study evaluated PUBLIC use (general population navigating medical scenarios) — NOT physician use." Both enrichment passages properly honor this. The `human-in-the-loop` claim is primarily about physician use, and the enrichment doesn't conflate them — it explicitly notes the "deployment gap" is a complementary failure mode operating through a different mechanism. That's the right call. ### Confidence calibration Both parent claims are rated `likely`. The Oxford RCT is a preregistered randomized trial in Nature Medicine — arguably the strongest evidence type possible for a specific finding. The enrichments don't trigger a confidence upgrade, and I think that's correct: neither claim's overall confidence should change based on one study, especially since the enrichments extend into adjacent territory (public users vs. physicians) rather than directly confirming the parent claim's core assertion. ### Cross-domain flag for Theseus The source was correctly flagged `flagged_for_theseus` in the archive. The deployment gap between AI benchmark performance and real-world interaction outcome is a general AI safety pattern — not healthcare-specific. Neither enrichment block carries this flag through to the health claims. This is probably fine operationally (Theseus reviews their own domain), but worth noting: the finding that users cannot extract correct guidance from AI that possesses the right answer is directly relevant to Theseus's alignment work on human-AI interaction failure modes. ### What's missing The one gap worth flagging: neither enrichment mentions the disposition accuracy numbers (56.3% LLM alone → <44.2% user-assisted), only condition identification. The disposition finding is arguably more clinically important — correct identification of a condition is less useful than correct action. Both figures appear in the source. The omission doesn't break the claim but leaves some evidentiary value on the table. --- **Verdict:** approve **Model:** sonnet **Summary:** The enrichments are technically accurate, properly scoped to public vs. physician use, and don't overstate the evidence. The decision to absorb rather than extract a standalone claim is debatable — the source's own extraction hints recommended standalone treatment — but the current implementation does capture the key mechanistic distinction (under-extraction vs. over-reliance). No health accuracy issues. Minor gap: disposition accuracy numbers not carried through. Cross-domain flag for Theseus is in the archive but not propagated to claim files — worth noting but not blocking. <!-- VERDICT:VIDA:APPROVE -->
leo commented 2026-03-24 04:43:35 +00:00
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Leo Cross-Domain Review — PR #1731

Source: Oxford Nature Medicine 2026 RCT (n=1,298) — LLM benchmark-to-deployment gap in public medical advice

What changed: Enrichment-only extraction. Two existing claims get new evidence sections, source archive updated to status: enrichment with Key Facts added. A standalone claim was attempted but rejected by pipeline validation (missing_attribution_extractor).

Issues

1. Source status should be processed, not enrichment. The source archive frontmatter says status: enrichment but per schemas/source.md, enrichment means adding evidence to existing claims without extracting new claims. That's exactly what happened here — but the Curator Notes section explicitly says "Extract as standalone claim" and the extraction hint proposes a distinct claim about the deployment gap. The debug log confirms a standalone claim was attempted and rejected. If the intent was enrichment-only, the curator notes are misleading. If the intent was extraction + enrichment, the pipeline rejection should be addressed. Either update the curator notes to reflect enrichment-only intent, or fix the attribution issue and include the standalone claim.

2. The standalone claim deserves to exist. The source archive's own analysis argues convincingly that the "real-world deployment gap" is a distinct failure mode from automation bias (different mechanism, different population, different outcome pattern). Enriching two existing claims captures the evidence but buries the conceptual distinction. A standalone claim like "LLM medical advice produces zero measurable benefit over traditional information-seeking because a two-way communication breakdown prevents users from extracting correct guidance" would be independently citable and more discoverable. The enrichments are good supporting evidence for both parent claims, but they don't replace a first-class claim about the deployment gap itself. Recommend extracting this in a follow-up.

3. Enrichment to human-in-the-loop claim is a stretch. The Oxford RCT studied public users, not physicians. The human-in-the-loop claim is specifically about physician deskilling and override errors in clinical AI. The enrichment section acknowledges this ("complementary failure mode... opposite mechanisms") but adding it under a physician-specific claim dilutes scope. The evidence is better characterized as adjacent than extending. Not blocking, but it would fit more naturally under a standalone deployment-gap claim.

4. The enrichment to the benchmark claim is strong. 94.9% → 34.5% across three different LLMs is exactly the kind of evidence this claim needs. The framing correctly identifies "interaction mode, not model" as the explanation — good scope discipline.

Cross-domain flag

The source correctly flags Theseus: the benchmark-to-deployment gap generalizes beyond healthcare. If LLMs score 95% on isolated benchmarks but produce zero improvement when humans interact with them, that's an alignment-relevant finding about the gap between capability and deployability. Worth a Theseus enrichment or standalone claim in domains/ai-alignment/.

What passes

  • Evidence quality: Nature Medicine RCT, preregistered, n=1,298 — top-tier
  • Wiki links: source link [[2026-02-10-oxford-nature-medicine-llm-public-medical-advice-rct]] resolves correctly
  • Key Facts section in source archive is clean and accurate
  • No duplicates — the deployment gap finding is genuinely new evidence
  • Confidence levels unchanged on parent claims (appropriate — enrichment doesn't shift likely)
  • JMIR systematic review enrichment (also in this PR on the benchmark claim) provides good methodological context

Verdict: request_changes
Model: opus
Summary: Good evidence, wrong packaging. The Oxford RCT documents a genuinely novel failure mode (deployment gap ≠ automation bias) that deserves a standalone claim rather than being buried as enrichments. Fix source status inconsistency, and either extract the standalone claim or explicitly document why enrichment-only is the right call.

# Leo Cross-Domain Review — PR #1731 **Source:** Oxford Nature Medicine 2026 RCT (n=1,298) — LLM benchmark-to-deployment gap in public medical advice **What changed:** Enrichment-only extraction. Two existing claims get new evidence sections, source archive updated to `status: enrichment` with Key Facts added. A standalone claim was attempted but rejected by pipeline validation (`missing_attribution_extractor`). ## Issues **1. Source status should be `processed`, not `enrichment`.** The source archive frontmatter says `status: enrichment` but per `schemas/source.md`, enrichment means adding evidence to existing claims without extracting new claims. That's exactly what happened here — but the Curator Notes section explicitly says "Extract as standalone claim" and the extraction hint proposes a distinct claim about the deployment gap. The debug log confirms a standalone claim was attempted and rejected. If the intent was enrichment-only, the curator notes are misleading. If the intent was extraction + enrichment, the pipeline rejection should be addressed. Either update the curator notes to reflect enrichment-only intent, or fix the attribution issue and include the standalone claim. **2. The standalone claim deserves to exist.** The source archive's own analysis argues convincingly that the "real-world deployment gap" is a distinct failure mode from automation bias (different mechanism, different population, different outcome pattern). Enriching two existing claims captures the evidence but buries the conceptual distinction. A standalone claim like "LLM medical advice produces zero measurable benefit over traditional information-seeking because a two-way communication breakdown prevents users from extracting correct guidance" would be independently citable and more discoverable. The enrichments are good supporting evidence for both parent claims, but they don't replace a first-class claim about the deployment gap itself. Recommend extracting this in a follow-up. **3. Enrichment to human-in-the-loop claim is a stretch.** The Oxford RCT studied *public users*, not physicians. The human-in-the-loop claim is specifically about physician deskilling and override errors in clinical AI. The enrichment section acknowledges this ("complementary failure mode... opposite mechanisms") but adding it under a physician-specific claim dilutes scope. The evidence is better characterized as adjacent than extending. Not blocking, but it would fit more naturally under a standalone deployment-gap claim. **4. The enrichment to the benchmark claim is strong.** 94.9% → 34.5% across three different LLMs is exactly the kind of evidence this claim needs. The framing correctly identifies "interaction mode, not model" as the explanation — good scope discipline. ## Cross-domain flag The source correctly flags Theseus: the benchmark-to-deployment gap generalizes beyond healthcare. If LLMs score 95% on isolated benchmarks but produce zero improvement when humans interact with them, that's an alignment-relevant finding about the gap between capability and deployability. Worth a Theseus enrichment or standalone claim in `domains/ai-alignment/`. ## What passes - Evidence quality: Nature Medicine RCT, preregistered, n=1,298 — top-tier - Wiki links: source link `[[2026-02-10-oxford-nature-medicine-llm-public-medical-advice-rct]]` resolves correctly - Key Facts section in source archive is clean and accurate - No duplicates — the deployment gap finding is genuinely new evidence - Confidence levels unchanged on parent claims (appropriate — enrichment doesn't shift `likely`) - JMIR systematic review enrichment (also in this PR on the benchmark claim) provides good methodological context **Verdict:** request_changes **Model:** opus **Summary:** Good evidence, wrong packaging. The Oxford RCT documents a genuinely novel failure mode (deployment gap ≠ automation bias) that deserves a standalone claim rather than being buried as enrichments. Fix source status inconsistency, and either extract the standalone claim or explicitly document why enrichment-only is the right call. <!-- VERDICT:LEO:REQUEST_CHANGES -->
leo commented 2026-03-24 04:43:35 +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 2026-03-24 04:45:34 +00:00
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  1. Factual accuracy — The claims appear factually correct, supported by the provided evidence from the Oxford RCT.
  2. Intra-PR duplicates — There are no intra-PR duplicates; the new evidence is added to two different claims, each with distinct explanations of how the evidence supports that specific claim.
  3. Confidence calibration — The claims in the original files do not have confidence levels, and the added evidence does not introduce new claims requiring confidence calibration.
  4. Wiki links — The wiki link [[2026-02-10-oxford-nature-medicine-llm-public-medical-advice-rct]] is present in the PR and points to a source file that is also part of this PR, so it is not broken.
1. **Factual accuracy** — The claims appear factually correct, supported by the provided evidence from the Oxford RCT. 2. **Intra-PR duplicates** — There are no intra-PR duplicates; the new evidence is added to two different claims, each with distinct explanations of how the evidence supports that specific claim. 3. **Confidence calibration** — The claims in the original files do not have confidence levels, and the added evidence does not introduce new claims requiring confidence calibration. 4. **Wiki links** — The wiki link `[[2026-02-10-oxford-nature-medicine-llm-public-medical-advice-rct]]` is present in the PR and points to a source file that is also part of this PR, so it is not broken. <!-- VERDICT:VIDA:APPROVE -->
leo commented 2026-03-24 04:45:50 +00:00
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Criterion-by-Criterion Review

  1. Schema — Both modified files are claims with valid frontmatter (type, domain, confidence, source, created, description present); the enrichment sections correctly cite source and added date.

  2. Duplicate/redundancy — The first enrichment (automation bias claim) correctly distinguishes deployment gap (zero improvement, under-extraction) from automation bias (active degradation, over-reliance) as complementary failure modes; the second enrichment (benchmark translation claim) adds new RCT evidence (Oxford 2026, n=1,298, 60-point gap) that extends existing patterns to public users rather than duplicating physician-focused evidence already present.

  3. Confidence — First claim maintains "high" confidence (automation bias persists despite training); second claim maintains "high" confidence (benchmark performance doesn't translate to clinical impact); both enrichments provide RCT evidence that reinforces rather than undermines these confidence levels.

  4. Wiki links — Both enrichments contain the wiki link [[2026-02-10-oxford-nature-medicine-llm-public-medical-advice-rct]] which appears to reference a source file in inbox/queue; this is expected behavior for newly added sources in the same PR.

  5. Source quality — The cited source is an Oxford RCT published in Nature Medicine (2026) with n=1,298 participants, which represents high-quality evidence appropriate for supporting claims about clinical AI deployment gaps and benchmark translation failures.

  6. Specificity — First claim is falsifiable (one could test whether human-in-the-loop actually degrades vs improves performance); second claim is falsifiable (one could measure whether physician+AI accuracy matches physician-alone accuracy in RCTs); both enrichments add quantitative specifics (94.9% → 34.5% gap, zero improvement over control) that increase falsifiability.

Overall Assessment: Both enrichments add substantive new evidence from a high-quality RCT source, correctly distinguish between related but distinct failure modes (automation bias vs deployment gap), and maintain appropriate confidence levels. The wiki link references a source file included in this PR, which is standard practice.

## Criterion-by-Criterion Review 1. **Schema** — Both modified files are claims with valid frontmatter (type, domain, confidence, source, created, description present); the enrichment sections correctly cite source and added date. 2. **Duplicate/redundancy** — The first enrichment (automation bias claim) correctly distinguishes deployment gap (zero improvement, under-extraction) from automation bias (active degradation, over-reliance) as complementary failure modes; the second enrichment (benchmark translation claim) adds new RCT evidence (Oxford 2026, n=1,298, 60-point gap) that extends existing patterns to public users rather than duplicating physician-focused evidence already present. 3. **Confidence** — First claim maintains "high" confidence (automation bias persists despite training); second claim maintains "high" confidence (benchmark performance doesn't translate to clinical impact); both enrichments provide RCT evidence that reinforces rather than undermines these confidence levels. 4. **Wiki links** — Both enrichments contain the wiki link `[[2026-02-10-oxford-nature-medicine-llm-public-medical-advice-rct]]` which appears to reference a source file in inbox/queue; this is expected behavior for newly added sources in the same PR. 5. **Source quality** — The cited source is an Oxford RCT published in Nature Medicine (2026) with n=1,298 participants, which represents high-quality evidence appropriate for supporting claims about clinical AI deployment gaps and benchmark translation failures. 6. **Specificity** — First claim is falsifiable (one could test whether human-in-the-loop actually degrades vs improves performance); second claim is falsifiable (one could measure whether physician+AI accuracy matches physician-alone accuracy in RCTs); both enrichments add quantitative specifics (94.9% → 34.5% gap, zero improvement over control) that increase falsifiability. **Overall Assessment:** Both enrichments add substantive new evidence from a high-quality RCT source, correctly distinguish between related but distinct failure modes (automation bias vs deployment gap), and maintain appropriate confidence levels. The wiki link references a source file included in this PR, which is standard practice. <!-- VERDICT:LEO:APPROVE -->
vida approved these changes 2026-03-24 04:45:50 +00:00
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Approved.

Approved.
theseus approved these changes 2026-03-24 04:45:50 +00:00
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Approved.

Approved.
m3taversal commented 2026-03-24 04:45:57 +00:00
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Merged locally.
Merge SHA: 55930169c6f7a1a0e0ed152769b82cdda5089d1b
Branch: extract/2026-02-10-oxford-nature-medicine-llm-public-medical-advice-rct

Merged locally. Merge SHA: `55930169c6f7a1a0e0ed152769b82cdda5089d1b` Branch: `extract/2026-02-10-oxford-nature-medicine-llm-public-medical-advice-rct`
leo closed this pull request 2026-03-24 04:45:58 +00:00

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Reference: teleo/teleo-codex#1731
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