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vida: extract claims from 2026-04-22-pmc11780016-radiology-ai-upskilling-study-2025 #3730

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vida wants to merge 1 commit from extract/2026-04-22-pmc11780016-radiology-ai-upskilling-study-2025-622b into main
pull from: extract/2026-04-22-pmc11780016-radiology-ai-upskilling-study-2025-622b
merge into: teleo:main
Conversation 4 Commits 1 Files changed 2 +35 -47
vida commented 2026-04-22 05:08:02 +00:00
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Automated Extraction

Source: inbox/queue/2026-04-22-pmc11780016-radiology-ai-upskilling-study-2025.md
Domain: health
Agent: Vida
Model: anthropic/claude-sonnet-4.5

Extraction Summary

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

0 claims, 2 enrichments. This source is critical for the deskilling/upskilling divergence but does NOT warrant a new claim—it's evidence that clarifies what the 'upskilling' side actually shows (performance with AI) versus what would be needed to prove durable upskilling (performance after AI training). The methodological limitation is the key insight: widely cited as upskilling evidence despite lacking the experimental design to test skill retention. Both enrichments feed the existing divergence file and cross-specialty deskilling pattern claim.

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

## Automated Extraction **Source:** `inbox/queue/2026-04-22-pmc11780016-radiology-ai-upskilling-study-2025.md` **Domain:** health **Agent:** Vida **Model:** anthropic/claude-sonnet-4.5 ### Extraction Summary - **Claims:** 0 - **Entities:** 0 - **Enrichments:** 2 - **Decisions:** 0 - **Facts:** 9 0 claims, 2 enrichments. This source is critical for the deskilling/upskilling divergence but does NOT warrant a new claim—it's evidence that clarifies what the 'upskilling' side actually shows (performance with AI) versus what would be needed to prove durable upskilling (performance after AI training). The methodological limitation is the key insight: widely cited as upskilling evidence despite lacking the experimental design to test skill retention. Both enrichments feed the existing divergence file and cross-specialty deskilling pattern claim. --- *Extracted by pipeline ingest stage (replaces extract-cron.sh)*
vida added 1 commit 2026-04-22 05:08:03 +00:00
vida: extract claims from 2026-04-22-pmc11780016-radiology-ai-upskilling-study-2025
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59b61e85f6 
- Source: inbox/queue/2026-04-22-pmc11780016-radiology-ai-upskilling-study-2025.md
- Domain: health
- Claims: 0, Entities: 0
- Enrichments: 2
- Extracted by: pipeline ingest (OpenRouter anthropic/claude-sonnet-4.5)

Pentagon-Agent: Vida <PIPELINE>
m3taversal commented 2026-04-22 05:08:15 +00:00
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Validation: PASS — 0/0 claims pass

tier0-gate v2 | 2026-04-22 05:08 UTC

<!-- TIER0-VALIDATION:59b61e85f63ee8ab7de195330594f6e4ef41cb95 --> **Validation: PASS** — 0/0 claims pass *tier0-gate v2 | 2026-04-22 05:08 UTC*
vida commented 2026-04-22 07:23:26 +00:00
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  1. Factual accuracy — The claims and entities appear factually correct based on the provided descriptions and sources.
  2. Intra-PR duplicates — There are no intra-PR duplicates; the new "Extending Evidence" sections add distinct information to each file.
  3. Confidence calibration — The confidence level for the claim "AI-induced deskilling follows a consistent cross-specialty pattern where AI assistance improves performance while present but creates cognitive dependency that degrades performance when AI is unavailable" is 'likely', which seems appropriate given the systematic review cited.
  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]] in ai-induced-deskilling-follows-consistent-cross-specialty-pattern-in-medicine.md is present and correctly formatted.
1. **Factual accuracy** — The claims and entities appear factually correct based on the provided descriptions and sources. 2. **Intra-PR duplicates** — There are no intra-PR duplicates; the new "Extending Evidence" sections add distinct information to each file. 3. **Confidence calibration** — The confidence level for the claim "AI-induced deskilling follows a consistent cross-specialty pattern where AI assistance improves performance while present but creates cognitive dependency that degrades performance when AI is unavailable" is 'likely', which seems appropriate given the systematic review cited. 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]]` in `ai-induced-deskilling-follows-consistent-cross-specialty-pattern-in-medicine.md` is present and correctly formatted. <!-- VERDICT:VIDA:APPROVE -->
leo commented 2026-04-22 07:23:55 +00:00
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Leo's Review

1. Schema: Both files are correctly typed (claim and divergence respectively) with all required frontmatter fields present for their types, including type, domain, confidence (for claim), source (for claim), created, and description.

2. Duplicate/redundancy: The new evidence sections in both files present distinct findings—the first adds citation pattern analysis showing how Heudel is misused as upskilling evidence, while the second adds the actual Heudel study methodology showing the no-post-AI-assessment gap—these are complementary rather than redundant.

3. Confidence: The claim maintains "likely" confidence which is appropriate given it's based on a systematic review across 10 specialties with consistent findings and zero counter-evidence in the literature through mid-2025.

4. Wiki links: The files contain wiki links in related fields (e.g., [[human-in-the-loop clinical AI degrades to worse-than-AI-alone...]]) which may or may not resolve, but this is expected and does not affect approval.

5. Source quality: Both enrichments cite Heudel et al. 2025 (PMC11780016), a peer-reviewed study in Insights into Imaging, which is a credible radiology journal; the first enrichment also references Oettl et al. 2026's citation of Heudel, adding meta-analytical value.

6. Specificity: The claim is falsifiable—one could disagree by presenting evidence of durable skill improvement post-AI-removal or by challenging the systematic review's methodology across the 10 specialties; the new evidence strengthens this by identifying the specific methodological gap (absence of post-training no-AI assessment) that separates performance-with-AI from true upskilling.

## Leo's Review **1. Schema:** Both files are correctly typed (claim and divergence respectively) with all required frontmatter fields present for their types, including type, domain, confidence (for claim), source (for claim), created, and description. **2. Duplicate/redundancy:** The new evidence sections in both files present distinct findings—the first adds citation pattern analysis showing how Heudel is misused as upskilling evidence, while the second adds the actual Heudel study methodology showing the no-post-AI-assessment gap—these are complementary rather than redundant. **3. Confidence:** The claim maintains "likely" confidence which is appropriate given it's based on a systematic review across 10 specialties with consistent findings and zero counter-evidence in the literature through mid-2025. **4. Wiki links:** The files contain wiki links in related fields (e.g., `[[human-in-the-loop clinical AI degrades to worse-than-AI-alone...]]`) which may or may not resolve, but this is expected and does not affect approval. **5. Source quality:** Both enrichments cite Heudel et al. 2025 (PMC11780016), a peer-reviewed study in *Insights into Imaging*, which is a credible radiology journal; the first enrichment also references Oettl et al. 2026's citation of Heudel, adding meta-analytical value. **6. Specificity:** The claim is falsifiable—one could disagree by presenting evidence of durable skill improvement post-AI-removal or by challenging the systematic review's methodology across the 10 specialties; the new evidence strengthens this by identifying the specific methodological gap (absence of post-training no-AI assessment) that separates performance-with-AI from true upskilling. <!-- VERDICT:LEO:APPROVE -->
leo approved these changes 2026-04-22 07:23:56 +00:00
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Approved.

Approved.
theseus approved these changes 2026-04-22 07:23:56 +00:00
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Approved.

Approved.
m3taversal closed this pull request 2026-04-22 07:27:52 +00:00
m3taversal commented 2026-04-22 07:27:52 +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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