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extract: 2025-10-15-cell-reports-medicine-llm-pharmacist-copilot-medication-safety

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inbox/queue/.extraction-debug/2025-10-15-cell-reports-medicine-llm-pharmacist-copilot-medication-safety.json Normal file
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@ -0,0 +1,24 @@
{
"rejected_claims": [
{
"filename": "llm-copilot-with-rag-architecture-improves-pharmacist-medication-error-detection-by-1-5x-for-serious-harm-cases.md",
"issues": [
"missing_attribution_extractor"
]
}
],
"validation_stats": {
"total": 1,
"kept": 0,
"fixed": 1,
"rejected": 1,
"fixes_applied": [
"llm-copilot-with-rag-architecture-improves-pharmacist-medication-error-detection-by-1-5x-for-serious-harm-cases.md:set_created:2026-03-24"
],
"rejections": [
"llm-copilot-with-rag-architecture-improves-pharmacist-medication-error-detection-by-1-5x-for-serious-harm-cases.md:missing_attribution_extractor"
]
},
"model": "anthropic/claude-sonnet-4.5",
"date": "2026-03-24"
}

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inbox/queue/2025-10-15-cell-reports-medicine-llm-pharmacist-copilot-medication-safety.md
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@ -7,9 +7,13 @@ date: 2025-10-15
domain: health
secondary_domains: [ai-alignment]
format: research-paper
status: unprocessed
status: null-result
priority: medium
tags: [clinical-ai-safety, centaur-model, medication-safety, llm-copilot, pharmacist, clinical-decision-support, rag, belief-5-counter-evidence]
processed_by: vida
processed_date: 2026-03-24
extraction_model: "anthropic/claude-sonnet-4.5"
extraction_notes: "LLM returned 1 claims, 1 rejected by validator"
---
## Content
@ -55,3 +59,11 @@ Published in *Cell Reports Medicine*, October 2025 (doi: 10.1016/j.xcrm.2025.003
PRIMARY CONNECTION: Belief 5 counter-evidence — centaur model works under specific conditions
WHY ARCHIVED: Best positive clinical AI safety evidence found across 12 sessions; establishes the conditions under which centaur design improves outcomes
EXTRACTION HINT: Extract with explicit scope constraint: centaur + RAG + structured safety task = works; general CDSS + automation bias mode = doesn't work per other evidence
## Key Facts
- Cell Reports Medicine published prospective cross-over study in October 2025 (doi: 10.1016/j.xcrm.2025.00396-9, PMC12629785)
- Study tested 91 error scenarios based on 40 clinical vignettes across 16 medical and surgical specialties
- Pharmacist + LLM co-pilot achieved 61% accuracy (precision 0.57, recall 0.61, F1 0.59)
- Co-pilot mode increased accuracy by 1.5-fold over pharmacist alone for serious harm errors specifically
- LLM-based CDSS used retrieval-augmented generation (RAG) framework with curated drug database