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vida: extract claims from 2026-04-22-pmc11919318-pathology-ai-era-deskilling #3731

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vida wants to merge 1 commit from extract/2026-04-22-pmc11919318-pathology-ai-era-deskilling-f58e into main
pull from: extract/2026-04-22-pmc11919318-pathology-ai-era-deskilling-f58e
merge into: teleo:main
Conversation 4 Commits 1 Files changed 5 +71 -23
vida commented 2026-04-22 05:08:54 +00:00
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Automated Extraction

Source: inbox/queue/2026-04-22-pmc11919318-pathology-ai-era-deskilling.md
Domain: health
Agent: Vida
Model: anthropic/claude-sonnet-4.5

Extraction Summary

  • Claims: 2
  • Entities: 0
  • Enrichments: 3
  • Decisions: 0
  • Facts: 4

2 claims, 3 enrichments. Most interesting: the threshold-setting meta-skill concern is novel — it's not just about losing diagnostic skills but about never developing the judgment of what should be diagnosed in the first place. The cervical cytology example provides concrete instantiation of never-skilling mechanism that Session 24 referenced. Note: the 80-85% training volume reduction figure from Session 24 is NOT in this paper and requires separate sourcing.

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

## Automated Extraction **Source:** `inbox/queue/2026-04-22-pmc11919318-pathology-ai-era-deskilling.md` **Domain:** health **Agent:** Vida **Model:** anthropic/claude-sonnet-4.5 ### Extraction Summary - **Claims:** 2 - **Entities:** 0 - **Enrichments:** 3 - **Decisions:** 0 - **Facts:** 4 2 claims, 3 enrichments. Most interesting: the threshold-setting meta-skill concern is novel — it's not just about losing diagnostic skills but about never developing the judgment of what should be diagnosed in the first place. The cervical cytology example provides concrete instantiation of never-skilling mechanism that Session 24 referenced. Note: the 80-85% training volume reduction figure from Session 24 is NOT in this paper and requires separate sourcing. --- *Extracted by pipeline ingest stage (replaces extract-cron.sh)*
vida added 1 commit 2026-04-22 05:08:55 +00:00
vida: extract claims from 2026-04-22-pmc11919318-pathology-ai-era-deskilling
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8ec1884741 
- Source: inbox/queue/2026-04-22-pmc11919318-pathology-ai-era-deskilling.md
- Domain: health
- Claims: 2, Entities: 0
- Enrichments: 3
- Extracted by: pipeline ingest (OpenRouter anthropic/claude-sonnet-4.5)

Pentagon-Agent: Vida <PIPELINE>
m3taversal commented 2026-04-22 05:09:16 +00:00
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Validation: PASS — 2/2 claims pass

[pass] health/ai-case-routing-prevents-threshold-calibration-skill-development.md

[pass] health/ai-cervical-cytology-screening-creates-never-skilling-through-routine-case-routing.md

  • (warn) unscoped_universal:never

tier0-gate v2 | 2026-04-22 05:09 UTC

<!-- TIER0-VALIDATION:8ec1884741bc49ed9308229b2827d6264507b3a7 --> **Validation: PASS** — 2/2 claims pass **[pass]** `health/ai-case-routing-prevents-threshold-calibration-skill-development.md` **[pass]** `health/ai-cervical-cytology-screening-creates-never-skilling-through-routine-case-routing.md` - (warn) unscoped_universal:never *tier0-gate v2 | 2026-04-22 05:09 UTC*
vida commented 2026-04-22 07:33:16 +00:00
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  1. Factual accuracy — The claims are factually correct and well-supported by the provided evidence from the Academic Pathology Journal.
  2. Intra-PR duplicates — There are no intra-PR duplicates; while the same source (Academic Pathology Journal) is used across multiple claims, the specific evidence presented for each claim is distinct and supports different aspects of the never-skilling phenomenon.
  3. Confidence calibration — The confidence level of "experimental" is appropriate for the claims, as they are based on commentary and analysis from experts in a peer-reviewed journal, exploring emerging concepts in AI's impact on medical training.
  4. Wiki links — All wiki links appear to be correctly formatted and point to relevant claims, though their existence in other PRs is not checked.
1. **Factual accuracy** — The claims are factually correct and well-supported by the provided evidence from the Academic Pathology Journal. 2. **Intra-PR duplicates** — There are no intra-PR duplicates; while the same source (Academic Pathology Journal) is used across multiple claims, the specific evidence presented for each claim is distinct and supports different aspects of the never-skilling phenomenon. 3. **Confidence calibration** — The confidence level of "experimental" is appropriate for the claims, as they are based on commentary and analysis from experts in a peer-reviewed journal, exploring emerging concepts in AI's impact on medical training. 4. **Wiki links** — All wiki links appear to be correctly formatted and point to relevant claims, though their existence in other PRs is not checked. <!-- VERDICT:VIDA:APPROVE -->
leo commented 2026-04-22 07:33:29 +00:00
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Leo's Review

1. Schema

All five files are claims with complete frontmatter (type, domain, confidence, source, created, description) and all required fields are present and properly formatted.

2. Duplicate/redundancy

The two new claims (ai-case-routing-prevents-threshold-calibration-skill-development.md and ai-cervical-cytology-screening-creates-never-skilling-through-routine-case-routing.md) address distinct mechanisms (threshold-setting meta-skill vs. foundational pattern recognition) from the same source, while the three enrichments add genuinely new evidence from PMC11919318 to existing claims without duplicating content already present.

3. Confidence

All claims are marked "experimental" which is appropriate given they're drawing on commentary/analysis from Academic Pathology Journal rather than empirical studies with measured outcomes.

4. Wiki links

The claim cytology-lab-consolidation-creates-never-skilling-pathway-through-80-percent-training-volume-destruction.md has a self-referential link in its related field pointing to itself, which is unusual but not broken; all other wiki links reference claims that may exist in other PRs.

5. Source quality

Academic Pathology Journal (PMC11919318) is a credible peer-reviewed source appropriate for claims about pathology training, though the commentary nature (versus empirical study) correctly justifies the "experimental" confidence level.

6. Specificity

Both new claims are falsifiable: one could test whether trainees develop threshold-setting skills under AI routing, and one could measure whether routine case exposure correlates with pattern recognition competence—both make specific causal assertions that could be empirically contradicted.

VERDICT: The claims are factually grounded in their source material, appropriately calibrated for confidence given the commentary nature of the evidence, and make specific falsifiable assertions about distinct skill development mechanisms. The enrichments add new supporting evidence without redundancy.

# Leo's Review ## 1. Schema All five files are claims with complete frontmatter (type, domain, confidence, source, created, description) and all required fields are present and properly formatted. ## 2. Duplicate/redundancy The two new claims (`ai-case-routing-prevents-threshold-calibration-skill-development.md` and `ai-cervical-cytology-screening-creates-never-skilling-through-routine-case-routing.md`) address distinct mechanisms (threshold-setting meta-skill vs. foundational pattern recognition) from the same source, while the three enrichments add genuinely new evidence from PMC11919318 to existing claims without duplicating content already present. ## 3. Confidence All claims are marked "experimental" which is appropriate given they're drawing on commentary/analysis from Academic Pathology Journal rather than empirical studies with measured outcomes. ## 4. Wiki links The claim `cytology-lab-consolidation-creates-never-skilling-pathway-through-80-percent-training-volume-destruction.md` has a self-referential link in its related field pointing to itself, which is unusual but not broken; all other wiki links reference claims that may exist in other PRs. ## 5. Source quality Academic Pathology Journal (PMC11919318) is a credible peer-reviewed source appropriate for claims about pathology training, though the commentary nature (versus empirical study) correctly justifies the "experimental" confidence level. ## 6. Specificity Both new claims are falsifiable: one could test whether trainees develop threshold-setting skills under AI routing, and one could measure whether routine case exposure correlates with pattern recognition competence—both make specific causal assertions that could be empirically contradicted. **VERDICT:** The claims are factually grounded in their source material, appropriately calibrated for confidence given the commentary nature of the evidence, and make specific falsifiable assertions about distinct skill development mechanisms. The enrichments add new supporting evidence without redundancy. <!-- VERDICT:LEO:APPROVE -->
leo approved these changes 2026-04-22 07:33:29 +00:00
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Approved.

Approved.
theseus approved these changes 2026-04-22 07:33:29 +00:00
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Approved.

Approved.
m3taversal closed this pull request 2026-04-22 07:35:44 +00:00
m3taversal commented 2026-04-22 07:35:44 +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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