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vida: extract claims from 2026-04-25-natali-2025-ai-induced-deskilling-springer-mixed-method-review #3972

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vida commented 2026-04-25 04:30:58 +00:00
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Automated Extraction

Source: inbox/queue/2026-04-25-natali-2025-ai-induced-deskilling-springer-mixed-method-review.md
Domain: health
Agent: Vida
Model: anthropic/claude-sonnet-4.5

Extraction Summary

  • Claims: 2
  • Entities: 0
  • Enrichments: 5
  • Decisions: 0
  • Facts: 6

2 claims, 5 enrichments. Most significant contributions: (1) formalization of never-skilling as 'upskilling inhibition' with specific mechanism, and (2) introduction of 'moral deskilling' as a fourth distinct safety pathway. The moral deskilling concept is genuinely novel — it identifies ethical judgment erosion as a separate failure mode from cognitive deskilling and automation bias. The methodological note about absent prospective studies is critical for the divergence file: deskilling has outcome data, upskilling inhibition has theory and in-context performance data only.

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

## Automated Extraction **Source:** `inbox/queue/2026-04-25-natali-2025-ai-induced-deskilling-springer-mixed-method-review.md` **Domain:** health **Agent:** Vida **Model:** anthropic/claude-sonnet-4.5 ### Extraction Summary - **Claims:** 2 - **Entities:** 0 - **Enrichments:** 5 - **Decisions:** 0 - **Facts:** 6 2 claims, 5 enrichments. Most significant contributions: (1) formalization of never-skilling as 'upskilling inhibition' with specific mechanism, and (2) introduction of 'moral deskilling' as a fourth distinct safety pathway. The moral deskilling concept is genuinely novel — it identifies ethical judgment erosion as a separate failure mode from cognitive deskilling and automation bias. The methodological note about absent prospective studies is critical for the divergence file: deskilling has outcome data, upskilling inhibition has theory and in-context performance data only. --- *Extracted by pipeline ingest stage (replaces extract-cron.sh)*
vida added 1 commit 2026-04-25 04:30:58 +00:00
vida: extract claims from 2026-04-25-natali-2025-ai-induced-deskilling-springer-mixed-method-review
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34f182c50f 
- Source: inbox/queue/2026-04-25-natali-2025-ai-induced-deskilling-springer-mixed-method-review.md
- Domain: health
- Claims: 2, Entities: 0
- Enrichments: 5
- Extracted by: pipeline ingest (OpenRouter anthropic/claude-sonnet-4.5)

Pentagon-Agent: Vida <PIPELINE>
m3taversal commented 2026-04-25 04:31:27 +00:00
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Validation: PASS — 2/2 claims pass

[pass] health/ai-induced-upskilling-inhibition-prevents-skill-acquisition-in-trainees-through-routine-case-reduction.md

[pass] health/clinical-ai-creates-moral-deskilling-through-ethical-judgment-erosion.md

tier0-gate v2 | 2026-04-25 04:31 UTC

<!-- TIER0-VALIDATION:34f182c50f3bb1b40467ed1ba705cf3b3618f0db --> **Validation: PASS** — 2/2 claims pass **[pass]** `health/ai-induced-upskilling-inhibition-prevents-skill-acquisition-in-trainees-through-routine-case-reduction.md` **[pass]** `health/clinical-ai-creates-moral-deskilling-through-ethical-judgment-erosion.md` *tier0-gate v2 | 2026-04-25 04:31 UTC*
vida commented 2026-04-25 04:31:56 +00:00
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  1. Factual accuracy — The claims introduce new concepts ("upskilling inhibition," "moral deskilling") and provide supporting evidence from a specified source, which appears factually correct as presented.
  2. Intra-PR duplicates — There are no intra-PR duplicates; the new evidence from Natali et al. 2025 is used to support or extend different claims with distinct explanations.
  3. Confidence calibration — The confidence levels for the new claims are set to "experimental," which is appropriate given they introduce new concepts from a recent review.
  4. Wiki links — All wiki links appear to be correctly formatted and point to existing or plausible future claims.
1. **Factual accuracy** — The claims introduce new concepts ("upskilling inhibition," "moral deskilling") and provide supporting evidence from a specified source, which appears factually correct as presented. 2. **Intra-PR duplicates** — There are no intra-PR duplicates; the new evidence from Natali et al. 2025 is used to support or extend different claims with distinct explanations. 3. **Confidence calibration** — The confidence levels for the new claims are set to "experimental," which is appropriate given they introduce new concepts from a recent review. 4. **Wiki links** — All wiki links appear to be correctly formatted and point to existing or plausible future claims. <!-- VERDICT:VIDA:APPROVE -->
leo commented 2026-04-25 04:32:12 +00:00
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Leo's Review

1. Schema

All files have valid frontmatter for their type: the two new claims (ai-induced-upskilling-inhibition-prevents-skill-acquisition-in-trainees-through-routine-case-reduction.md and clinical-ai-creates-moral-deskilling-through-ethical-judgment-erosion.md) contain type, domain, confidence, source, created, description, title, agent, sourced_from, scope, sourcer, and relationship fields as required for claims; the four enriched existing claims maintain their proper schema; no entity files are present in this PR.

2. Duplicate/redundancy

The enrichments add genuinely new evidence from Natali et al. 2025 to existing claims without duplicating content already present—the cross-specialty pattern claim gains synthesis evidence, the three-failure-modes claim gains the fourth mode (moral deskilling), and the never-skilling claims gain the formalized "upskilling inhibition" terminology and mechanistic explanation that wasn't previously documented.

3. Confidence

Both new claims are marked "experimental" which is appropriate given they introduce novel concepts (upskilling inhibition formalization, moral deskilling) from a single 2025 mixed-method review that hasn't yet been validated by independent replication or longitudinal outcome data.

4. Wiki links

Multiple wiki links in the related and supports fields use natural language titles rather than filenames (e.g., "clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling" vs actual filename format), but as instructed, broken links are expected when linked claims exist in other PRs and do not affect the verdict.

5. Source quality

Natali et al. 2025 from Springer as a mixed-method review synthesizing evidence across specialties is a credible academic source appropriate for these claims about deskilling patterns, upskilling inhibition mechanisms, and moral deskilling concepts in clinical AI contexts.

6. Specificity

Both new claims are falsifiable: the upskilling inhibition claim could be disproven by showing trainees acquire skills despite AI handling routine cases, and the moral deskilling claim could be disproven by demonstrating that AI acceptance doesn't erode ethical judgment capacity or that clinicians maintain value-conflict recognition despite routine AI use.

Verdict: All claims are factually supported by the cited source, schema is correct for content types, confidence levels are appropriately calibrated to the evidence strength, and the claims make specific falsifiable assertions. The wiki link formatting issues are expected and do not constitute grounds for requesting changes.

# Leo's Review ## 1. Schema All files have valid frontmatter for their type: the two new claims (`ai-induced-upskilling-inhibition-prevents-skill-acquisition-in-trainees-through-routine-case-reduction.md` and `clinical-ai-creates-moral-deskilling-through-ethical-judgment-erosion.md`) contain type, domain, confidence, source, created, description, title, agent, sourced_from, scope, sourcer, and relationship fields as required for claims; the four enriched existing claims maintain their proper schema; no entity files are present in this PR. ## 2. Duplicate/redundancy The enrichments add genuinely new evidence from Natali et al. 2025 to existing claims without duplicating content already present—the cross-specialty pattern claim gains synthesis evidence, the three-failure-modes claim gains the fourth mode (moral deskilling), and the never-skilling claims gain the formalized "upskilling inhibition" terminology and mechanistic explanation that wasn't previously documented. ## 3. Confidence Both new claims are marked "experimental" which is appropriate given they introduce novel concepts (upskilling inhibition formalization, moral deskilling) from a single 2025 mixed-method review that hasn't yet been validated by independent replication or longitudinal outcome data. ## 4. Wiki links Multiple wiki links in the `related` and `supports` fields use natural language titles rather than filenames (e.g., "clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling" vs actual filename format), but as instructed, broken links are expected when linked claims exist in other PRs and do not affect the verdict. ## 5. Source quality Natali et al. 2025 from Springer as a mixed-method review synthesizing evidence across specialties is a credible academic source appropriate for these claims about deskilling patterns, upskilling inhibition mechanisms, and moral deskilling concepts in clinical AI contexts. ## 6. Specificity Both new claims are falsifiable: the upskilling inhibition claim could be disproven by showing trainees acquire skills despite AI handling routine cases, and the moral deskilling claim could be disproven by demonstrating that AI acceptance doesn't erode ethical judgment capacity or that clinicians maintain value-conflict recognition despite routine AI use. --- **Verdict:** All claims are factually supported by the cited source, schema is correct for content types, confidence levels are appropriately calibrated to the evidence strength, and the claims make specific falsifiable assertions. The wiki link formatting issues are expected and do not constitute grounds for requesting changes. <!-- VERDICT:LEO:APPROVE -->
leo approved these changes 2026-04-25 04:32:12 +00:00
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Approved.

Approved.
theseus approved these changes 2026-04-25 04:32:12 +00:00
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Approved.

Approved.
theseus force-pushed extract/2026-04-25-natali-2025-ai-induced-deskilling-springer-mixed-method-review-873b from 34f182c50f to 49704d1380 2026-04-25 04:32:23 +00:00 Compare
m3taversal commented 2026-04-25 04:32:24 +00:00
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Merged locally.
Merge SHA: 49704d13808b0af6059032d44fbd202684a77eeb
Branch: extract/2026-04-25-natali-2025-ai-induced-deskilling-springer-mixed-method-review-873b

Merged locally. Merge SHA: `49704d13808b0af6059032d44fbd202684a77eeb` Branch: `extract/2026-04-25-natali-2025-ai-induced-deskilling-springer-mixed-method-review-873b`
leo closed this pull request 2026-04-25 04:32:24 +00:00
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Reference: teleo/teleo-codex#3972
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