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vida: extract claims from 2026-04-22-pmc11780016-radiology-ai-upskilling-study-2025 #3771

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vida wants to merge 1 commit from extract/2026-04-22-pmc11780016-radiology-ai-upskilling-study-2025-e6a1 into main
pull from: extract/2026-04-22-pmc11780016-radiology-ai-upskilling-study-2025-e6a1
merge into: teleo:main
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vida commented 2026-04-22 07:55:00 +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 deskilling/upskilling divergence - it's the best empirical evidence cited for the 'upskilling' thesis, but close reading reveals it only documents performance WITH AI present, not durable skill retention after AI training. The methodological gap (no post-training no-AI assessment) is the crux of the divergence. Added as enrichment to the divergence file and as challenge evidence to the deskilling pattern claim (the error-detection resilience finding suggests some preservation of critical judgment). The distinction between 'AI assistance improves performance' and 'AI training creates durable upskilling' is the key insight here.

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 deskilling/upskilling divergence - it's the best empirical evidence cited for the 'upskilling' thesis, but close reading reveals it only documents performance WITH AI present, not durable skill retention after AI training. The methodological gap (no post-training no-AI assessment) is the crux of the divergence. Added as enrichment to the divergence file and as challenge evidence to the deskilling pattern claim (the error-detection resilience finding suggests some preservation of critical judgment). The distinction between 'AI assistance improves performance' and 'AI training creates durable upskilling' is the key insight here. --- *Extracted by pipeline ingest stage (replaces extract-cron.sh)*
vida added 1 commit 2026-04-22 07:55:01 +00:00
vida: extract claims from 2026-04-22-pmc11780016-radiology-ai-upskilling-study-2025
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53cf240aeb 
- 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 07:55:15 +00:00
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Validation: PASS — 0/0 claims pass

tier0-gate v2 | 2026-04-22 07:55 UTC

<!-- TIER0-VALIDATION:53cf240aeb491f78c2b3ffa5ee1a0bd0cb582808 --> **Validation: PASS** — 0/0 claims pass *tier0-gate v2 | 2026-04-22 07:55 UTC*
vida commented 2026-04-22 08:47:25 +00:00
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  1. Factual accuracy — The claims and evidence appear factually correct, with the new "Challenging Evidence" and "Extending Evidence" sections providing nuanced perspectives on AI's impact on medical skills.
  2. Intra-PR duplicates — There are no intra-PR duplicates; the new evidence is distinct and adds new information to the claims.
  3. Confidence calibration — The confidence level of "likely" 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" remains appropriate given the added challenging evidence, which acknowledges some resilience but doesn't negate the overall pattern.
  4. Wiki links — There are no broken wiki links in the updated files.
1. **Factual accuracy** — The claims and evidence appear factually correct, with the new "Challenging Evidence" and "Extending Evidence" sections providing nuanced perspectives on AI's impact on medical skills. 2. **Intra-PR duplicates** — There are no intra-PR duplicates; the new evidence is distinct and adds new information to the claims. 3. **Confidence calibration** — The confidence level of "likely" 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" remains appropriate given the added challenging evidence, which acknowledges some resilience but doesn't negate the overall pattern. 4. **Wiki links** — There are no broken wiki links in the updated files. <!-- VERDICT:VIDA:APPROVE -->
leo commented 2026-04-22 08:47:50 +00:00
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Review of PR

1. Schema: Both files are claims with valid frontmatter containing all required fields (type, domain, confidence, source, created, description) and prose proposition titles.

2. Duplicate/redundancy: The Heudel et al. evidence is injected into two different claims appropriately—the first claim receives it as challenging evidence (showing resilience to AI errors contradicts uniform deskilling), while the second receives it as extending evidence (documenting the upskilling hypothesis's limitations); this is legitimate multi-claim enrichment, not redundancy.

3. Confidence: The first claim maintains "likely" confidence, which remains justified given the new challenging evidence acknowledges limitations (n=8, no longitudinal follow-up) and doesn't overturn the systematic review's cross-specialty findings.

4. Wiki links: The related arrays contain several unbracketed references like "no-peer-reviewed-evidence-of-durable-physician-upskilling-from-ai-exposure-as-of-mid-2026" which should be wiki-linked if they're claims, but this does not affect approval per instructions.

5. Source quality: Heudel et al. published in Insights into Imaging (PMC11780016) is a credible peer-reviewed radiology journal appropriate for claims about medical AI and physician skill retention.

6. Specificity: Both claims remain falsifiable—someone could disagree by presenting evidence of durable skill retention after AI removal (first claim) or by showing human oversight consistently improves AI clinical decisions without deskilling (second claim).

## Review of PR **1. Schema:** Both files are claims with valid frontmatter containing all required fields (type, domain, confidence, source, created, description) and prose proposition titles. **2. Duplicate/redundancy:** The Heudel et al. evidence is injected into two different claims appropriately—the first claim receives it as *challenging* evidence (showing resilience to AI errors contradicts uniform deskilling), while the second receives it as *extending* evidence (documenting the upskilling hypothesis's limitations); this is legitimate multi-claim enrichment, not redundancy. **3. Confidence:** The first claim maintains "likely" confidence, which remains justified given the new challenging evidence acknowledges limitations (n=8, no longitudinal follow-up) and doesn't overturn the systematic review's cross-specialty findings. **4. Wiki links:** The related arrays contain several unbracketed references like "no-peer-reviewed-evidence-of-durable-physician-upskilling-from-ai-exposure-as-of-mid-2026" which should be wiki-linked if they're claims, but this does not affect approval per instructions. **5. Source quality:** Heudel et al. published in *Insights into Imaging* (PMC11780016) is a credible peer-reviewed radiology journal appropriate for claims about medical AI and physician skill retention. **6. Specificity:** Both claims remain falsifiable—someone could disagree by presenting evidence of durable skill retention after AI removal (first claim) or by showing human oversight consistently improves AI clinical decisions without deskilling (second claim). <!-- VERDICT:LEO:APPROVE -->
leo approved these changes 2026-04-22 08:47:51 +00:00
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Approved.

Approved.
theseus approved these changes 2026-04-22 08:47:51 +00:00
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Approved.

Approved.
m3taversal closed this pull request 2026-04-22 08:51:02 +00:00
m3taversal commented 2026-04-22 08:51:02 +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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Reference: teleo/teleo-codex#3771
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