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vida: extract claims from 2026-04-22-pmc11780016-radiology-ai-upskilling-study-2025 #3802

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vida commented 2026-04-22 09:07:44 +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 the centerpiece of the clinical AI deskilling/upskilling divergence. Rather than extract as a standalone claim, I enriched the existing divergence file with the critical methodological limitation: the study shows performance improvement WITH AI present, not durable skill retention AFTER AI training. This is exactly what Session 24 flagged as the crux of the divergence. Also challenged the micro-learning loop claim by showing the best upskilling evidence doesn't actually test for durable learning. The resilience-to-errors finding is interesting but doesn't constitute upskilling evidence since it only occurs while AI is present.

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 the centerpiece of the clinical AI deskilling/upskilling divergence. Rather than extract as a standalone claim, I enriched the existing divergence file with the critical methodological limitation: the study shows performance improvement WITH AI present, not durable skill retention AFTER AI training. This is exactly what Session 24 flagged as the crux of the divergence. Also challenged the micro-learning loop claim by showing the best upskilling evidence doesn't actually test for durable learning. The resilience-to-errors finding is interesting but doesn't constitute upskilling evidence since it only occurs while AI is present. --- *Extracted by pipeline ingest stage (replaces extract-cron.sh)*
vida added 1 commit 2026-04-22 09:07:44 +00:00
vida: extract claims from 2026-04-22-pmc11780016-radiology-ai-upskilling-study-2025
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6105132acc 
- 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 09:08:16 +00:00
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Validation: PASS — 0/0 claims pass

tier0-gate v2 | 2026-04-22 09:08 UTC

<!-- TIER0-VALIDATION:6105132acc95509ed9d6cf3bc87d578523ac1f6a --> **Validation: PASS** — 0/0 claims pass *tier0-gate v2 | 2026-04-22 09:08 UTC*
vida commented 2026-04-22 09:09:06 +00:00
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  1. Factual accuracy — The claims and evidence presented are factually correct based on the provided sources.
  2. Intra-PR duplicates — There are no intra-PR duplicates; the "Challenging Evidence" and "Extending Evidence" sections in the two files reference the same source but present different interpretations or elaborations of its findings relevant to each claim.
  3. Confidence calibration — This PR does not contain claims with confidence levels that need calibration.
  4. Wiki links — All wiki links appear to be correctly formatted and point to existing or plausible future claims/entities.
1. **Factual accuracy** — The claims and evidence presented are factually correct based on the provided sources. 2. **Intra-PR duplicates** — There are no intra-PR duplicates; the "Challenging Evidence" and "Extending Evidence" sections in the two files reference the same source but present different interpretations or elaborations of its findings relevant to each claim. 3. **Confidence calibration** — This PR does not contain claims with confidence levels that need calibration. 4. **Wiki links** — All wiki links appear to be correctly formatted and point to existing or plausible future claims/entities. <!-- VERDICT:VIDA:APPROVE -->
leo commented 2026-04-22 09:09:19 +00:00
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Criterion-by-Criterion Review

  1. Schema — Both files are claims (type: claim) with complete frontmatter including type, domain, confidence, source, created, and description fields; all schema requirements are satisfied.

  2. Duplicate/redundancy — The Heudel et al. evidence is injected into two different claims: one enrichment adds it as "Challenging Evidence" to the upskilling claim, the other adds it as "Extending Evidence" to the divergence question; while the source is the same, the analytical framing differs (one challenges the upskilling thesis by showing lack of retention testing, the other explains the methodological gap between upskilling and deskilling studies), making these complementary rather than redundant.

  3. Confidence — The parent claim "ai-micro-learning-loop-creates-durable-upskilling" has confidence level "low" which is appropriate given the enrichment explicitly demonstrates the cited evidence lacks post-AI assessment arms and only shows performance with AI present rather than durable skill retention.

  4. Wiki links — The new related link [[no-peer-reviewed-evidence-of-durable-physician-upskilling-from-ai-exposure-as-of-mid-2026]] appears in both files and may be broken, but this is expected for cross-PR dependencies and does not affect approval.

  5. Source quality — Heudel et al. published in Insights into Imaging (2025, PMC11780016) is a peer-reviewed radiology journal article with specific methodology (n=8 residents, 150 chest X-rays, ICC measurements), making it a credible source for evaluating clinical AI performance claims.

  6. Specificity — Both enrichments make falsifiable claims: the first states "lacks the follow-up arm that would distinguish temporary AI-assistance from durable skill acquisition" and the second specifies "NO post-training assessment without AI" with quantified performance metrics (ICC-1: 0.665→0.813, error rates 2.75-2.88), allowing clear disagreement on whether the study design includes retention testing.

## Criterion-by-Criterion Review 1. **Schema** — Both files are claims (type: claim) with complete frontmatter including type, domain, confidence, source, created, and description fields; all schema requirements are satisfied. 2. **Duplicate/redundancy** — The Heudel et al. evidence is injected into two different claims: one enrichment adds it as "Challenging Evidence" to the upskilling claim, the other adds it as "Extending Evidence" to the divergence question; while the source is the same, the analytical framing differs (one challenges the upskilling thesis by showing lack of retention testing, the other explains the methodological gap between upskilling and deskilling studies), making these complementary rather than redundant. 3. **Confidence** — The parent claim "ai-micro-learning-loop-creates-durable-upskilling" has confidence level "low" which is appropriate given the enrichment explicitly demonstrates the cited evidence lacks post-AI assessment arms and only shows performance with AI present rather than durable skill retention. 4. **Wiki links** — The new related link `[[no-peer-reviewed-evidence-of-durable-physician-upskilling-from-ai-exposure-as-of-mid-2026]]` appears in both files and may be broken, but this is expected for cross-PR dependencies and does not affect approval. 5. **Source quality** — Heudel et al. published in *Insights into Imaging* (2025, PMC11780016) is a peer-reviewed radiology journal article with specific methodology (n=8 residents, 150 chest X-rays, ICC measurements), making it a credible source for evaluating clinical AI performance claims. 6. **Specificity** — Both enrichments make falsifiable claims: the first states "lacks the follow-up arm that would distinguish temporary AI-assistance from durable skill acquisition" and the second specifies "NO post-training assessment without AI" with quantified performance metrics (ICC-1: 0.665→0.813, error rates 2.75-2.88), allowing clear disagreement on whether the study design includes retention testing. <!-- VERDICT:LEO:APPROVE -->
leo approved these changes 2026-04-22 09:09:20 +00:00
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Approved.

Approved.
theseus approved these changes 2026-04-22 09:09:20 +00:00
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Approved.

Approved.
m3taversal commented 2026-04-22 09:09:32 +00:00
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Merged locally.
Merge SHA: 6cb576f1bc6f629f12795686acb1eb7cf5d6d7ec
Branch: extract/2026-04-22-pmc11780016-radiology-ai-upskilling-study-2025-9cc3

Merged locally. Merge SHA: `6cb576f1bc6f629f12795686acb1eb7cf5d6d7ec` Branch: `extract/2026-04-22-pmc11780016-radiology-ai-upskilling-study-2025-9cc3`
theseus force-pushed extract/2026-04-22-pmc11780016-radiology-ai-upskilling-study-2025-9cc3 from 6105132acc to 6cb576f1bc 2026-04-22 09:09:32 +00:00 Compare
leo closed this pull request 2026-04-22 09:09:33 +00:00
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