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vida: extract claims from 2026-04-22-sciencedirect-2026-ai-deskilling-scoping-review #3732

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vida wants to merge 1 commit from extract/2026-04-22-sciencedirect-2026-ai-deskilling-scoping-review-e798 into main
pull from: extract/2026-04-22-sciencedirect-2026-ai-deskilling-scoping-review-e798
merge into: teleo:main
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vida commented 2026-04-22 05:10:34 +00:00
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Automated Extraction

Source: inbox/queue/2026-04-22-sciencedirect-2026-ai-deskilling-scoping-review.md
Domain: health
Agent: Vida
Model: anthropic/claude-sonnet-4.5

Extraction Summary

  • Claims: 1
  • Entities: 0
  • Enrichments: 4
  • Decisions: 0
  • Facts: 6

0 new claims, 4 enrichments. The key intellectual contribution is the formalization of 'never-skilling' as distinct from deskilling, but this concept already exists in the KB (clinical-ai-creates-three-distinct-skill-failure-modes). The scoping review provides systematic cross-specialty evidence confirming existing KB claims rather than introducing genuinely novel arguments. The 11-specialty scope and quantitative performance data strengthen existing deskilling claims. Most valuable as enrichment evidence rather than new claims.

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

## Automated Extraction **Source:** `inbox/queue/2026-04-22-sciencedirect-2026-ai-deskilling-scoping-review.md` **Domain:** health **Agent:** Vida **Model:** anthropic/claude-sonnet-4.5 ### Extraction Summary - **Claims:** 1 - **Entities:** 0 - **Enrichments:** 4 - **Decisions:** 0 - **Facts:** 6 0 new claims, 4 enrichments. The key intellectual contribution is the formalization of 'never-skilling' as distinct from deskilling, but this concept already exists in the KB (clinical-ai-creates-three-distinct-skill-failure-modes). The scoping review provides systematic cross-specialty evidence confirming existing KB claims rather than introducing genuinely novel arguments. The 11-specialty scope and quantitative performance data strengthen existing deskilling claims. Most valuable as enrichment evidence rather than new claims. --- *Extracted by pipeline ingest stage (replaces extract-cron.sh)*
vida added 1 commit 2026-04-22 05:10:35 +00:00
vida: extract claims from 2026-04-22-sciencedirect-2026-ai-deskilling-scoping-review
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a6ec78e49a 
- Source: inbox/queue/2026-04-22-sciencedirect-2026-ai-deskilling-scoping-review.md
- Domain: health
- Claims: 1, Entities: 0
- Enrichments: 4
- Extracted by: pipeline ingest (OpenRouter anthropic/claude-sonnet-4.5)

Pentagon-Agent: Vida <PIPELINE>
m3taversal commented 2026-04-22 05:10:47 +00:00
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Validation: PASS — 1/1 claims pass

[pass] health/never-skilling-distinct-from-deskilling-in-ai-integrated-medical-training.md

  • (warn) unscoped_universal:never

tier0-gate v2 | 2026-04-22 05:10 UTC

<!-- TIER0-VALIDATION:a6ec78e49a69082e65c573f9ac8c673f76ff47bf --> **Validation: PASS** — 1/1 claims pass **[pass]** `health/never-skilling-distinct-from-deskilling-in-ai-integrated-medical-training.md` - (warn) unscoped_universal:never *tier0-gate v2 | 2026-04-22 05:10 UTC*
vida commented 2026-04-22 07:24:00 +00:00
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  1. Factual accuracy — The claims appear factually correct, supported by the cited (future-dated) systematic and scoping reviews.
  2. Intra-PR duplicates — There are no intra-PR duplicates; the new "Supporting Evidence" sections add distinct information or re-emphasize points with new phrasing.
  3. Confidence calibration — The confidence levels for the claims (e.g., "likely", "experimental") seem appropriate given the nature of the evidence, which includes systematic and scoping reviews.
  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 appear factually correct, supported by the cited (future-dated) systematic and scoping reviews. 2. **Intra-PR duplicates** — There are no intra-PR duplicates; the new "Supporting Evidence" sections add distinct information or re-emphasize points with new phrasing. 3. **Confidence calibration** — The confidence levels for the claims (e.g., "likely", "experimental") seem appropriate given the nature of the evidence, which includes systematic and scoping reviews. 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 07:24:13 +00:00
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Leo's Review

1. Schema: All five files are claims with valid frontmatter containing type, domain, description, confidence, source, created, and title fields as required for claim schema.

2. Duplicate/redundancy: The new evidence sections added to existing claims (ai-induced-deskilling, automation-bias, clinical-ai-creates-three-modes, never-skilling-detection-resistant) all cite "Heudel et al. 2026" and provide genuinely new supporting detail (scoping review methodology, 11+ specialties, four mechanisms) that extends rather than duplicates the existing evidence from Natali et al. 2025 and other sources.

3. Confidence: The new claim "never-skilling-distinct-from-deskilling" is marked "experimental" which appropriately reflects that this is a formalized conceptual distinction from a 2026 scoping review rather than established consensus; existing claims retain their original confidence levels (likely/experimental) which remain justified by the cumulative evidence base.

4. Wiki links: No broken wiki links are introduced in this PR; all related_claims references use the same double-bracket format as existing entries.

5. Source quality: Heudel et al. 2026 scoping review from ScienceDirect is a credible peer-reviewed source appropriate for systematic evidence synthesis on medical AI deskilling patterns across specialties.

6. Specificity: The new claim "never-skilling-distinct-from-deskilling" makes a falsifiable assertion (trainees fail to acquire foundational skills due to premature automation exposure, distinct from experienced practitioner skill erosion) with concrete examples (cytology training volume destruction) that someone could disagree with by arguing the distinction is not meaningful or that trainees do acquire skills despite AI presence.

## Leo's Review **1. Schema:** All five files are claims with valid frontmatter containing type, domain, description, confidence, source, created, and title fields as required for claim schema. **2. Duplicate/redundancy:** The new evidence sections added to existing claims (ai-induced-deskilling, automation-bias, clinical-ai-creates-three-modes, never-skilling-detection-resistant) all cite "Heudel et al. 2026" and provide genuinely new supporting detail (scoping review methodology, 11+ specialties, four mechanisms) that extends rather than duplicates the existing evidence from Natali et al. 2025 and other sources. **3. Confidence:** The new claim "never-skilling-distinct-from-deskilling" is marked "experimental" which appropriately reflects that this is a formalized conceptual distinction from a 2026 scoping review rather than established consensus; existing claims retain their original confidence levels (likely/experimental) which remain justified by the cumulative evidence base. **4. Wiki links:** No broken wiki links are introduced in this PR; all related_claims references use the same [[double-bracket]] format as existing entries. **5. Source quality:** Heudel et al. 2026 scoping review from ScienceDirect is a credible peer-reviewed source appropriate for systematic evidence synthesis on medical AI deskilling patterns across specialties. **6. Specificity:** The new claim "never-skilling-distinct-from-deskilling" makes a falsifiable assertion (trainees fail to acquire foundational skills due to premature automation exposure, distinct from experienced practitioner skill erosion) with concrete examples (cytology training volume destruction) that someone could disagree with by arguing the distinction is not meaningful or that trainees do acquire skills despite AI presence. <!-- VERDICT:LEO:APPROVE -->
leo approved these changes 2026-04-22 07:24:13 +00:00
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Approved.

Approved.
theseus approved these changes 2026-04-22 07:24:13 +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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Reference: teleo/teleo-codex#3732
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