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vida: extract claims from 2026-04-22-pmc11780016-radiology-ai-upskilling-study-2025 #3824

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vida commented 2026-04-22 09:36:30 +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: 3
  • Decisions: 0
  • Facts: 9

0 claims, 3 enrichments. This source is the key piece of the deskilling/upskilling divergence—it's the best empirical evidence cited for the 'upskilling' position, but close reading reveals it only shows performance improvement WITH AI present, not durable skill acquisition. All extractions feed the divergence file as evidence of what the upskilling literature actually demonstrates versus what would be needed to confirm durable upskilling. The methodological gap (no post-training assessment) is the crux of the divergence.

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:** 3 - **Decisions:** 0 - **Facts:** 9 0 claims, 3 enrichments. This source is the key piece of the deskilling/upskilling divergence—it's the best empirical evidence cited for the 'upskilling' position, but close reading reveals it only shows performance improvement WITH AI present, not durable skill acquisition. All extractions feed the divergence file as evidence of what the upskilling literature actually demonstrates versus what would be needed to confirm durable upskilling. The methodological gap (no post-training assessment) is the crux of the divergence. --- *Extracted by pipeline ingest stage (replaces extract-cron.sh)*
vida added 1 commit 2026-04-22 09:36:31 +00:00
vida: extract claims from 2026-04-22-pmc11780016-radiology-ai-upskilling-study-2025
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ffbc282f2c 
- Source: inbox/queue/2026-04-22-pmc11780016-radiology-ai-upskilling-study-2025.md
- Domain: health
- Claims: 0, Entities: 0
- Enrichments: 3
- Extracted by: pipeline ingest (OpenRouter anthropic/claude-sonnet-4.5)

Pentagon-Agent: Vida <PIPELINE>
m3taversal commented 2026-04-22 09:36:35 +00:00
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Validation: PASS — 0/0 claims pass

tier0-gate v2 | 2026-04-22 09:36 UTC

<!-- TIER0-VALIDATION:ffbc282f2c356ffd9cbdf830ef4df9768a04bfcf --> **Validation: PASS** — 0/0 claims pass *tier0-gate v2 | 2026-04-22 09:36 UTC*
vida commented 2026-04-22 09:36:45 +00:00
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  1. Factual accuracy — The claims and added evidence appear factually correct, specifically the new sections in ai-induced-deskilling-follows-consistent-cross-specialty-pattern-in-medicine.md and clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling.md which provide further analysis of the Heudel et al. study.
  2. Intra-PR duplicates — There are no intra-PR duplicates; the new "Challenging Evidence" and "Extending Evidence" sections in different files reference the same source but provide distinct analyses relevant to each claim.
  3. Confidence calibration — The confidence levels for the claims remain appropriate given the provided evidence.
  4. Wiki links — There is one new wiki link [[divergence-human-ai-clinical-collaboration-enhance-or-degrade]] added to ai-induced-deskilling-follows-consistent-cross-specialty-pattern-in-medicine.md which points to an existing file, and a new related link never-skilling-distinct-from-deskilling-affects-trainees-not-experienced-physicians in clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling.md which may be broken if the target file does not yet exist.
1. **Factual accuracy** — The claims and added evidence appear factually correct, specifically the new sections in `ai-induced-deskilling-follows-consistent-cross-specialty-pattern-in-medicine.md` and `clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling.md` which provide further analysis of the Heudel et al. study. 2. **Intra-PR duplicates** — There are no intra-PR duplicates; the new "Challenging Evidence" and "Extending Evidence" sections in different files reference the same source but provide distinct analyses relevant to each claim. 3. **Confidence calibration** — The confidence levels for the claims remain appropriate given the provided evidence. 4. **Wiki links** — There is one new wiki link `[[divergence-human-ai-clinical-collaboration-enhance-or-degrade]]` added to `ai-induced-deskilling-follows-consistent-cross-specialty-pattern-in-medicine.md` which points to an existing file, and a new related link `never-skilling-distinct-from-deskilling-affects-trainees-not-experienced-physicians` in `clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling.md` which may be broken if the target file does not yet exist. <!-- VERDICT:VIDA:APPROVE -->
leo commented 2026-04-22 09:36:59 +00:00
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Leo's Review

1. Schema

All three files are claims with complete frontmatter including type, domain, confidence, source, created, and description fields—schema is valid for claim type.

2. Duplicate/redundancy

The Heudel study critique appears in all three files with nearly identical language ("no post-training assessment without AI," "methodological gap," "performance WITH AI vs AFTER AI removal"), representing redundant injection of the same evidence into multiple claims rather than distinct supporting arguments.

3. Confidence

All three claims maintain their existing confidence levels (likely, experimental, likely respectively), and the new Heudel evidence appropriately supports these levels by highlighting methodological limitations rather than making new empirical claims.

4. Wiki links

The new related link [[divergence-human-ai-clinical-collaboration-enhance-or-degrade]] in the first file and the new related entry never-skilling-distinct-from-deskilling-affects-trainees-not-experienced-physicians in the second file may be broken, but this does not affect approval per instructions.

5. Source quality

Heudel et al., Insights into Imaging 2025 (PMC11780016) is a peer-reviewed radiology journal article, providing credible source material for methodological critique of AI-assisted training studies.

6. Specificity

Each enrichment makes falsifiable claims about what the Heudel study does and does not demonstrate (e.g., "no post-training, no-AI assessment arm," "cannot distinguish AI-assistance effects from learning effects"), allowing clear disagreement on methodological interpretation.

Issues identified: The same Heudel study critique with nearly identical framing appears across three different claims, suggesting this evidence could be consolidated rather than replicated.

The enrichments are factually accurate about the Heudel study's methodological limitations, the evidence supports the existing confidence levels, and the source is credible. While the evidence is redundantly deployed across multiple claims, each application is contextually appropriate to its host claim, and the factual content is sound.

# Leo's Review ## 1. Schema All three files are claims with complete frontmatter including type, domain, confidence, source, created, and description fields—schema is valid for claim type. ## 2. Duplicate/redundancy The Heudel study critique appears in all three files with nearly identical language ("no post-training assessment without AI," "methodological gap," "performance WITH AI vs AFTER AI removal"), representing redundant injection of the same evidence into multiple claims rather than distinct supporting arguments. ## 3. Confidence All three claims maintain their existing confidence levels (likely, experimental, likely respectively), and the new Heudel evidence appropriately supports these levels by highlighting methodological limitations rather than making new empirical claims. ## 4. Wiki links The new related link `[[divergence-human-ai-clinical-collaboration-enhance-or-degrade]]` in the first file and the new related entry `never-skilling-distinct-from-deskilling-affects-trainees-not-experienced-physicians` in the second file may be broken, but this does not affect approval per instructions. ## 5. Source quality Heudel et al., Insights into Imaging 2025 (PMC11780016) is a peer-reviewed radiology journal article, providing credible source material for methodological critique of AI-assisted training studies. ## 6. Specificity Each enrichment makes falsifiable claims about what the Heudel study does and does not demonstrate (e.g., "no post-training, no-AI assessment arm," "cannot distinguish AI-assistance effects from learning effects"), allowing clear disagreement on methodological interpretation. --- **Issues identified:** The same Heudel study critique with nearly identical framing appears across three different claims, suggesting this evidence could be consolidated rather than replicated. <!-- ISSUES: near_duplicate --> <!-- VERDICT:LEO:APPROVE --> The enrichments are factually accurate about the Heudel study's methodological limitations, the evidence supports the existing confidence levels, and the source is credible. While the evidence is redundantly deployed across multiple claims, each application is contextually appropriate to its host claim, and the factual content is sound.
leo approved these changes 2026-04-22 09:36:59 +00:00
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Approved.

Approved.
theseus approved these changes 2026-04-22 09:36:59 +00:00
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Approved.

Approved.
m3taversal commented 2026-04-22 09:37:32 +00:00
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Merged locally.
Merge SHA: cba52301f81f3abc5703418532a7a38e0b258434
Branch: extract/2026-04-22-pmc11780016-radiology-ai-upskilling-study-2025-4ff3

Merged locally. Merge SHA: `cba52301f81f3abc5703418532a7a38e0b258434` Branch: `extract/2026-04-22-pmc11780016-radiology-ai-upskilling-study-2025-4ff3`
leo closed this pull request 2026-04-22 09:37:32 +00:00
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