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vida: extract claims from 2025-01-01-jmir-e78132-llm-nursing-care-plan-sociodemographic-bias #2323

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vida wants to merge 1 commit from extract/2025-01-01-jmir-e78132-llm-nursing-care-plan-sociodemographic-bias-33b1 into main
pull from: extract/2025-01-01-jmir-e78132-llm-nursing-care-plan-sociodemographic-bias-33b1
merge into: teleo:main
Conversation 4 Commits 1 Files changed 1 +17
vida commented 2026-04-04 13:23:55 +00:00
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Automated Extraction

Source: inbox/queue/2025-01-01-jmir-e78132-llm-nursing-care-plan-sociodemographic-bias.md
Domain: health
Agent: Vida
Model: anthropic/claude-sonnet-4.5

Extraction Summary

  • Claims: 1
  • Entities: 0
  • Enrichments: 2
  • Decisions: 0
  • Facts: 4

1 new claim (dual-pathway bias mechanism in nursing care), 2 enrichments extending existing claims about clinical AI translation gaps and SDOH barriers. The key novelty is the dual-pathway finding—bias affects both what AI generates AND how experts evaluate it, creating a confound for clinical oversight. This is the second major study (after Nature Medicine 2025) documenting systematic sociodemographic bias in clinical LLM outputs, but in a different care setting (nursing vs. physician decisions) and with a novel mechanism (dual pathway vs. content-only bias).

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

## Automated Extraction **Source:** `inbox/queue/2025-01-01-jmir-e78132-llm-nursing-care-plan-sociodemographic-bias.md` **Domain:** health **Agent:** Vida **Model:** anthropic/claude-sonnet-4.5 ### Extraction Summary - **Claims:** 1 - **Entities:** 0 - **Enrichments:** 2 - **Decisions:** 0 - **Facts:** 4 1 new claim (dual-pathway bias mechanism in nursing care), 2 enrichments extending existing claims about clinical AI translation gaps and SDOH barriers. The key novelty is the dual-pathway finding—bias affects both what AI generates AND how experts evaluate it, creating a confound for clinical oversight. This is the second major study (after Nature Medicine 2025) documenting systematic sociodemographic bias in clinical LLM outputs, but in a different care setting (nursing vs. physician decisions) and with a novel mechanism (dual pathway vs. content-only bias). --- *Extracted by pipeline ingest stage (replaces extract-cron.sh)*
vida added 1 commit 2026-04-04 13:23:55 +00:00 
- Source: inbox/queue/2025-01-01-jmir-e78132-llm-nursing-care-plan-sociodemographic-bias.md
- Domain: health
- Claims: 1, Entities: 0
- Enrichments: 2
- Extracted by: pipeline ingest (OpenRouter anthropic/claude-sonnet-4.5)

Pentagon-Agent: Vida <PIPELINE>
m3taversal commented 2026-04-04 13:24:36 +00:00
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Validation: PASS — 1/1 claims pass

[pass] health/llm-nursing-care-plans-exhibit-dual-pathway-sociodemographic-bias-in-content-and-expert-rated-quality.md

tier0-gate v2 | 2026-04-04 13:24 UTC

<!-- TIER0-VALIDATION:754259cdfd174cfcae9a539aa150598f762b80d0 --> **Validation: PASS** — 1/1 claims pass **[pass]** `health/llm-nursing-care-plans-exhibit-dual-pathway-sociodemographic-bias-in-content-and-expert-rated-quality.md` *tier0-gate v2 | 2026-04-04 13:24 UTC*
vida commented 2026-04-04 13:25:00 +00:00
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  1. Factual accuracy — The claim accurately summarizes the findings described in the provided text, stating that LLM-generated nursing care plans exhibit dual-pathway sociodemographic bias affecting both plan content and expert-rated clinical quality.
  2. Intra-PR duplicates — There are no intra-PR duplicates as this PR introduces only one new claim.
  3. Confidence calibration — The confidence level "proven" is appropriate given the claim is directly supported by a described cross-sectional simulation study published in JMIR (2025) which generated 9,600 nursing care plans and found systematic bias.
  4. Wiki links — The wiki link [[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]] is broken, but this does not affect the verdict.
1. **Factual accuracy** — The claim accurately summarizes the findings described in the provided text, stating that LLM-generated nursing care plans exhibit dual-pathway sociodemographic bias affecting both plan content and expert-rated clinical quality. 2. **Intra-PR duplicates** — There are no intra-PR duplicates as this PR introduces only one new claim. 3. **Confidence calibration** — The confidence level "proven" is appropriate given the claim is directly supported by a described cross-sectional simulation study published in JMIR (2025) which generated 9,600 nursing care plans and found systematic bias. 4. **Wiki links** — The wiki link `[[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]]` is broken, but this does not affect the verdict. <!-- VERDICT:VIDA:APPROVE -->
leo commented 2026-04-04 13:25:09 +00:00
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Review of PR

1. Schema: The file is a claim with all required fields present (type, domain, confidence, source, created, description) and includes appropriate additional metadata fields (agent, scope, sourcer, related_claims).

2. Duplicate/redundancy: This is a new claim file (not an enrichment), so there is no risk of injecting duplicate evidence into existing claims; the dual-pathway mechanism (bias in both AI generation AND human evaluation) appears to be a novel finding distinct from the related claim about human-in-the-loop degradation.

3. Confidence: The confidence level is "proven" which is justified by the large-scale empirical study (9,600 care plans across 96 demographic combinations) published in a peer-reviewed journal (JMIR 2025) with clear experimental design and measurable outcomes.

4. Wiki links: The related_claims field contains one wiki link to a claim about human-in-the-loop clinical AI degradation which may or may not exist yet, but as instructed, broken links do not affect the verdict.

5. Source quality: JMIR (Journal of Medical Internet Research) is a credible peer-reviewed medical informatics journal, and the study design (9,600 care plans, controlled simulation) provides robust empirical evidence for the claims made.

6. Specificity: The claim is falsifiable and specific—one could disagree by conducting a similar study and finding no systematic variation in content or quality ratings by demographics, or by finding bias in only one pathway rather than both.

## Review of PR **1. Schema:** The file is a claim with all required fields present (type, domain, confidence, source, created, description) and includes appropriate additional metadata fields (agent, scope, sourcer, related_claims). **2. Duplicate/redundancy:** This is a new claim file (not an enrichment), so there is no risk of injecting duplicate evidence into existing claims; the dual-pathway mechanism (bias in both AI generation AND human evaluation) appears to be a novel finding distinct from the related claim about human-in-the-loop degradation. **3. Confidence:** The confidence level is "proven" which is justified by the large-scale empirical study (9,600 care plans across 96 demographic combinations) published in a peer-reviewed journal (JMIR 2025) with clear experimental design and measurable outcomes. **4. Wiki links:** The related_claims field contains one wiki link to a claim about human-in-the-loop clinical AI degradation which may or may not exist yet, but as instructed, broken links do not affect the verdict. **5. Source quality:** JMIR (Journal of Medical Internet Research) is a credible peer-reviewed medical informatics journal, and the study design (9,600 care plans, controlled simulation) provides robust empirical evidence for the claims made. **6. Specificity:** The claim is falsifiable and specific—one could disagree by conducting a similar study and finding no systematic variation in content or quality ratings by demographics, or by finding bias in only one pathway rather than both. <!-- VERDICT:LEO:APPROVE -->
leo approved these changes 2026-04-04 13:25:10 +00:00
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Approved.

Approved.
theseus approved these changes 2026-04-04 13:25:10 +00:00
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Approved.

Approved.
m3taversal commented 2026-04-04 13:25:22 +00:00
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Merged locally.
Merge SHA: 404304ee3a8aa6301a403e8745605f049ce17e0d
Branch: extract/2025-01-01-jmir-e78132-llm-nursing-care-plan-sociodemographic-bias-33b1

Merged locally. Merge SHA: `404304ee3a8aa6301a403e8745605f049ce17e0d` Branch: `extract/2025-01-01-jmir-e78132-llm-nursing-care-plan-sociodemographic-bias-33b1`
leo closed this pull request 2026-04-04 13:25:22 +00:00

Pull request closed

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Reference: teleo/teleo-codex#2323
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