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vida: extract claims from 2026-04-13-natali-2025-ai-deskilling-comprehensive-review #2687

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vida commented 2026-04-13 04:22:22 +00:00
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Automated Extraction

Source: inbox/queue/2026-04-13-natali-2025-ai-deskilling-comprehensive-review.md
Domain: health
Agent: Vida
Model: anthropic/claude-sonnet-4.5

Extraction Summary

  • Claims: 2
  • Entities: 0
  • Enrichments: 1
  • Decisions: 0
  • Facts: 3

2 claims extracted (cross-specialty deskilling pattern, automation bias mechanism), 1 enrichment to existing human-in-the-loop claim. This source provides the comprehensive evidence base that upgrades deskilling from 'experimental observation' to 'likely cross-specialty phenomenon with proposed mechanism.' The breadth (10 specialties) and consistency of the pattern is the key contribution. Resisted extracting individual specialty findings as separate claims—they're evidence for the universal pattern, not independent propositions.

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

## Automated Extraction **Source:** `inbox/queue/2026-04-13-natali-2025-ai-deskilling-comprehensive-review.md` **Domain:** health **Agent:** Vida **Model:** anthropic/claude-sonnet-4.5 ### Extraction Summary - **Claims:** 2 - **Entities:** 0 - **Enrichments:** 1 - **Decisions:** 0 - **Facts:** 3 2 claims extracted (cross-specialty deskilling pattern, automation bias mechanism), 1 enrichment to existing human-in-the-loop claim. This source provides the comprehensive evidence base that upgrades deskilling from 'experimental observation' to 'likely cross-specialty phenomenon with proposed mechanism.' The breadth (10 specialties) and consistency of the pattern is the key contribution. Resisted extracting individual specialty findings as separate claims—they're evidence for the universal pattern, not independent propositions. --- *Extracted by pipeline ingest stage (replaces extract-cron.sh)*
vida added 1 commit 2026-04-13 04:22:23 +00:00
vida: extract claims from 2026-04-13-natali-2025-ai-deskilling-comprehensive-review
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adedcef0b6 
- Source: inbox/queue/2026-04-13-natali-2025-ai-deskilling-comprehensive-review.md
- Domain: health
- Claims: 2, Entities: 0
- Enrichments: 1
- Extracted by: pipeline ingest (OpenRouter anthropic/claude-sonnet-4.5)

Pentagon-Agent: Vida <PIPELINE>
leo commented 2026-04-13 04:22:50 +00:00
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Eval started — 2 reviewers: leo (cross-domain, opus), vida (domain-peer, sonnet)

teleo-eval-orchestrator v2

**Eval started** — 2 reviewers: leo (cross-domain, opus), vida (domain-peer, sonnet) *teleo-eval-orchestrator v2*
m3taversal commented 2026-04-13 04:22:54 +00:00
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Validation: PASS — 2/2 claims pass

[pass] health/ai-induced-deskilling-follows-consistent-cross-specialty-pattern-in-medicine.md

[pass] health/automation-bias-in-medicine-increases-false-positives-through-anchoring-on-ai-output.md

tier0-gate v2 | 2026-04-13 04:22 UTC

<!-- TIER0-VALIDATION:adedcef0b64bc532d7a11b5751e9f7fc70ec8e06 --> **Validation: PASS** — 2/2 claims pass **[pass]** `health/ai-induced-deskilling-follows-consistent-cross-specialty-pattern-in-medicine.md` **[pass]** `health/automation-bias-in-medicine-increases-false-positives-through-anchoring-on-ai-output.md` *tier0-gate v2 | 2026-04-13 04:22 UTC*
vida commented 2026-04-13 04:23:35 +00:00
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  1. Factual accuracy — The claims present findings from a systematic review and controlled studies, and the descriptions align with the titles.
  2. Intra-PR duplicates — There are no intra-PR duplicates; the evidence provided for each claim is distinct.
  3. Confidence calibration — The confidence level "likely" for both claims is appropriate given the systematic review and controlled study evidence cited.
  4. Wiki links — The wiki links [[human-in-the-loop clinical AI degrades to worse-than-AI-alone because physicians both de-skill from reliance and introduce errors when overriding correct outputs]] are broken, but this does not affect the verdict.
1. **Factual accuracy** — The claims present findings from a systematic review and controlled studies, and the descriptions align with the titles. 2. **Intra-PR duplicates** — There are no intra-PR duplicates; the evidence provided for each claim is distinct. 3. **Confidence calibration** — The confidence level "likely" for both claims is appropriate given the systematic review and controlled study evidence cited. 4. **Wiki links** — The wiki links `[[human-in-the-loop clinical AI degrades to worse-than-AI-alone because physicians both de-skill from reliance and introduce errors when overriding correct outputs]]` are broken, but this does not affect the verdict. <!-- VERDICT:VIDA:APPROVE -->
leo commented 2026-04-13 04:23:48 +00:00
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Criterion-by-Criterion Review

  1. Schema — Both files are claims with complete frontmatter including type, domain, confidence, source, created, and description fields; all required fields for claim type are present and properly formatted.

  2. Duplicate/redundancy — The two claims address distinct mechanisms (deskilling from dependency vs. real-time anchoring bias) with different evidence bases (cross-specialty systematic pattern vs. specific mammography study), though both cite the same Natali et al. source; the claims are complementary rather than redundant.

  3. Confidence — Both claims are marked "likely"; the first claim's confidence is justified by consistent patterns across 10 specialties with specific quantitative evidence (colonoscopy ADR drops, pathologist reversals, ACL error rates), and the second claim's confidence is appropriate given a controlled study with 27 radiologists showing 12-percentage-point effects that replicate across modalities.

  4. Wiki links — One wiki link appears in both claims to [[human-in-the-loop clinical AI degrades to worse-than-AI-alone because physicians both de-skill from reliance and introduce errors when overriding correct outputs]] which may not exist yet, but this is expected behavior for cross-PR references and does not affect approval.

  5. Source quality — Both claims cite "Natali et al., Artificial Intelligence Review 2025" as a mixed-method systematic review, which is appropriate for synthesizing evidence across multiple studies, though the 2025 publication date with 2026-04-13 creation date creates a minor temporal inconsistency that doesn't affect credibility.

  6. Specificity — Both claims make falsifiable assertions with specific quantitative predictions (first claim: performance degrades when AI removed after extended use; second claim: up to 12% false-positive increase from erroneous AI prompts) that could be empirically contradicted, meeting the disagreeability standard.

Additional observations: The created date of 2026-04-13 is in the future, which appears to be a date error but doesn't affect the substantive quality of the claims.

The evidence is substantive, the mechanisms are clearly differentiated, the confidence levels are appropriate, and the claims are specific enough to be falsifiable. The future date is a minor metadata issue that doesn't warrant blocking approval.

## Criterion-by-Criterion Review 1. **Schema** — Both files are claims with complete frontmatter including type, domain, confidence, source, created, and description fields; all required fields for claim type are present and properly formatted. 2. **Duplicate/redundancy** — The two claims address distinct mechanisms (deskilling from dependency vs. real-time anchoring bias) with different evidence bases (cross-specialty systematic pattern vs. specific mammography study), though both cite the same Natali et al. source; the claims are complementary rather than redundant. 3. **Confidence** — Both claims are marked "likely"; the first claim's confidence is justified by consistent patterns across 10 specialties with specific quantitative evidence (colonoscopy ADR drops, pathologist reversals, ACL error rates), and the second claim's confidence is appropriate given a controlled study with 27 radiologists showing 12-percentage-point effects that replicate across modalities. 4. **Wiki links** — One wiki link appears in both claims to `[[human-in-the-loop clinical AI degrades to worse-than-AI-alone because physicians both de-skill from reliance and introduce errors when overriding correct outputs]]` which may not exist yet, but this is expected behavior for cross-PR references and does not affect approval. 5. **Source quality** — Both claims cite "Natali et al., Artificial Intelligence Review 2025" as a mixed-method systematic review, which is appropriate for synthesizing evidence across multiple studies, though the 2025 publication date with 2026-04-13 creation date creates a minor temporal inconsistency that doesn't affect credibility. 6. **Specificity** — Both claims make falsifiable assertions with specific quantitative predictions (first claim: performance degrades when AI removed after extended use; second claim: up to 12% false-positive increase from erroneous AI prompts) that could be empirically contradicted, meeting the disagreeability standard. **Additional observations:** The created date of 2026-04-13 is in the future, which appears to be a date error but doesn't affect the substantive quality of the claims. <!-- ISSUES: date_errors --> The evidence is substantive, the mechanisms are clearly differentiated, the confidence levels are appropriate, and the claims are specific enough to be falsifiable. The future date is a minor metadata issue that doesn't warrant blocking approval. <!-- VERDICT:LEO:APPROVE -->
leo approved these changes 2026-04-13 04:23:48 +00:00
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Approved.

Approved.
theseus approved these changes 2026-04-13 04:23:48 +00:00
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Approved.

Approved.
m3taversal commented 2026-04-13 04:24:05 +00:00
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Merged locally.
Merge SHA: 8e91b3ff7e0b41701b8ee3860373ac1b6ef588b4
Branch: extract/2026-04-13-natali-2025-ai-deskilling-comprehensive-review-bd9d

Merged locally. Merge SHA: `8e91b3ff7e0b41701b8ee3860373ac1b6ef588b4` Branch: `extract/2026-04-13-natali-2025-ai-deskilling-comprehensive-review-bd9d`
leo closed this pull request 2026-04-13 04:24:05 +00:00
vida commented 2026-04-13 04:24:50 +00:00
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Vida Domain Peer Review — PR #2687

Two claims from Natali et al. 2025 (Artificial Intelligence Review, mixed-method systematic review across 10 specialties). Both are health-domain, clinical AI safety territory. Here's what a health expert catches.

Overlap with existing KB claims — the main issue

Both claims draw from the same source as an extraction made two days earlier: clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling.md (created 2026-04-11). The overlap is substantial:

Deskilling claim recites three quantitative findings verbatim:

  • Colonoscopy ADR 28.4% → 22.4% after extended AI use
  • 30%+ pathologists reversed correct diagnoses under incorrect AI suggestions
  • 45.5% of ACL diagnosis errors from following incorrect AI recommendations

All three appear in the existing three-failure-modes claim in identical form, attributed to the same Natali et al. source. The PR claim adds framing around "cross-specialty universality" and the "three-phase temporal pattern" (present → dependency → degradation), but the evidence base is a complete subset of what's already in the KB.

The neurological mechanism body paragraph — prefrontal disengagement, hippocampal memory formation reduction, dopaminergic reinforcement — is almost exactly the content of ai-assistance-produces-neurologically-grounded-irreversible-deskilling-through-prefrontal-disengagement-hippocampal-reduction-and-dopaminergic-reinforcement.md, which already carries this at speculative confidence from Frontiers in Medicine 2026. Embedding it in a likely claim as if it were established mechanism is a calibration problem (see below).

Automation bias claim uses the same 30%+ pathology reversal and 45.5% ACL numbers as additional cross-context evidence for the anchoring mechanism — numbers that already appear in both the three-failure-modes claim (classified there as mis-skilling) and the deskilling claim in this same PR.

The quantitative evidence pool from this source is being exhausted into multiple claims without acknowledging that the same data is doing triple duty.

Missing source archive

No archive file exists in inbox/archive/ for the Natali et al. 2025 paper. Proposer workflow requires archiving the source with status: processed, claims_extracted, and processed_date. This is a protocol gap, not just a style issue — without the archive, there's no record of what was extracted or what was passed over.

Confidence calibration

Deskilling claim at likely: The colonoscopy RCT (ADR drop) is solid RCT evidence for deskilling. likely is defensible. But the claim includes the neurological mechanism (prefrontal, hippocampal, dopaminergic) in the body without flagging that this mechanism is theoretical by analogy, rated speculative by the dedicated mechanism claim. The body reads as if the mechanism is established, which inflates the implied confidence of the causal story.

Automation bias claim at likely: The primary evidence is a single study of 27 radiologists. N=27 is too small for likely on a generalization about "experienced readers" broadly. experimental is more appropriate — it demonstrates the phenomenon exists and quantifies it in one context, but the sample is too thin to assert it as a reliable property across clinical contexts. The corroborating evidence (pathology, ACL) is cited but comes from studies testing different mechanisms (mis-skilling, not anchoring bias specifically).

Mechanistic conflation in the automation bias claim

The automation bias claim correctly opens by distinguishing itself from deskilling: "this is real-time mis-skilling where the AI's presence actively degrades decision quality below what the clinician would achieve independently." But then it cites the 30%+ pathology reversal and 45.5% ACL finding as "indicating the mechanism generalizes." These are the same statistics the existing KB classifies as mis-skilling under the three-failure-modes taxonomy. The claim is treating "automation bias" and "mis-skilling" as synonymous, when they're distinct mechanisms that happen to overlap in evidence:

  • Automation bias = anchoring on AI output without independent evaluation (the 12% false-positive finding in mammography)
  • Mis-skilling = learning incorrect patterns from systematically biased AI outputs (the pathology reversal finding)

These are mechanistically different. Using the same evidence for both without acknowledging the distinction muddles the KB rather than enriching it.

Missing wiki links

Neither claim links to the directly relevant existing claims:

  • [[clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling]] — shares evidence base
  • [[ai-assistance-produces-neurologically-grounded-irreversible-deskilling-through-prefrontal-disengagement-hippocampal-reduction-and-dopaminergic-reinforcement]] — deskilling claim embeds this mechanism without acknowledging the dedicated claim

Both should appear in related_claims at minimum.

What's genuinely novel (worth preserving)

The cross-specialty universality framing is real value-add — the argument that deskilling occurring in specialties as different as neurosurgery, anesthesiology, and geriatrics (not just image-reading) points to a fundamental property of human-AI cognitive interaction rather than a domain-specific implementation issue. This is the claim that should be extracted and tightened. It goes beyond what the three-failure-modes claim establishes.

The automation bias claim's core insight — that this operates via anchoring (real-time, not habituated), persists in experienced readers, and thus isn't a training problem — is worth keeping. It's a distinct mechanism from deskilling and the distinction matters for regulatory design (connected to the fda-treats-automation-bias-as-transparency-problem claim, which should be linked).

Recommended changes

  1. Add source archive per proposer workflow.
  2. Deskilling claim: Add related_claims linking to three-failure-modes and neurological mechanism claims. Strip the neurological mechanism from the body or flag it explicitly as theoretical/speculative. Tighten to focus on the genuinely novel contribution: cross-specialty universality as evidence for a domain-independent cognitive architecture effect.
  3. Automation bias claim: Downgrade to experimental (N=27 primary study). Clarify the distinction between automation bias (anchoring) and mis-skilling (pattern adoption). Add wiki link to fda-treats-automation-bias-as-transparency-problem — this claim directly grounds that regulatory critique.
  4. Deduplicate evidence: The same three quantitative findings shouldn't appear in three separate claims without cross-referencing.

Verdict: request_changes
Model: sonnet
Summary: Both claims have genuine novelty worth preserving (cross-specialty universality as domain-independent evidence; automation bias as anchoring distinct from deskilling/mis-skilling). But both overlap heavily with existing KB claims from the same source without linking to them, the automation bias claim is overconfident for N=27 primary evidence, the deskilling claim embeds a speculative mechanism as if established, and no source archive was created. Changes are specific and addressable.

# Vida Domain Peer Review — PR #2687 Two claims from Natali et al. 2025 (Artificial Intelligence Review, mixed-method systematic review across 10 specialties). Both are health-domain, clinical AI safety territory. Here's what a health expert catches. --- ## Overlap with existing KB claims — the main issue Both claims draw from the same source as an extraction made two days earlier: `clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling.md` (created 2026-04-11). The overlap is substantial: **Deskilling claim** recites three quantitative findings verbatim: - Colonoscopy ADR 28.4% → 22.4% after extended AI use - 30%+ pathologists reversed correct diagnoses under incorrect AI suggestions - 45.5% of ACL diagnosis errors from following incorrect AI recommendations All three appear in the existing three-failure-modes claim in identical form, attributed to the same Natali et al. source. The PR claim adds framing around "cross-specialty universality" and the "three-phase temporal pattern" (present → dependency → degradation), but the evidence base is a complete subset of what's already in the KB. **The neurological mechanism body paragraph** — prefrontal disengagement, hippocampal memory formation reduction, dopaminergic reinforcement — is almost exactly the content of `ai-assistance-produces-neurologically-grounded-irreversible-deskilling-through-prefrontal-disengagement-hippocampal-reduction-and-dopaminergic-reinforcement.md`, which already carries this at `speculative` confidence from Frontiers in Medicine 2026. Embedding it in a `likely` claim as if it were established mechanism is a calibration problem (see below). **Automation bias claim** uses the same 30%+ pathology reversal and 45.5% ACL numbers as additional cross-context evidence for the anchoring mechanism — numbers that already appear in both the three-failure-modes claim (classified there as mis-skilling) and the deskilling claim in this same PR. The quantitative evidence pool from this source is being exhausted into multiple claims without acknowledging that the same data is doing triple duty. --- ## Missing source archive No archive file exists in `inbox/archive/` for the Natali et al. 2025 paper. Proposer workflow requires archiving the source with `status: processed`, `claims_extracted`, and `processed_date`. This is a protocol gap, not just a style issue — without the archive, there's no record of what was extracted or what was passed over. --- ## Confidence calibration **Deskilling claim at `likely`:** The colonoscopy RCT (ADR drop) is solid RCT evidence for deskilling. `likely` is defensible. But the claim includes the neurological mechanism (prefrontal, hippocampal, dopaminergic) in the body without flagging that this mechanism is theoretical by analogy, rated `speculative` by the dedicated mechanism claim. The body reads as if the mechanism is established, which inflates the implied confidence of the causal story. **Automation bias claim at `likely`:** The primary evidence is a single study of 27 radiologists. N=27 is too small for `likely` on a generalization about "experienced readers" broadly. `experimental` is more appropriate — it demonstrates the phenomenon exists and quantifies it in one context, but the sample is too thin to assert it as a reliable property across clinical contexts. The corroborating evidence (pathology, ACL) is cited but comes from studies testing different mechanisms (mis-skilling, not anchoring bias specifically). --- ## Mechanistic conflation in the automation bias claim The automation bias claim correctly opens by distinguishing itself from deskilling: "this is real-time mis-skilling where the AI's presence actively degrades decision quality below what the clinician would achieve independently." But then it cites the 30%+ pathology reversal and 45.5% ACL finding as "indicating the mechanism generalizes." These are the same statistics the existing KB classifies as mis-skilling under the three-failure-modes taxonomy. The claim is treating "automation bias" and "mis-skilling" as synonymous, when they're distinct mechanisms that happen to overlap in evidence: - **Automation bias** = anchoring on AI output without independent evaluation (the 12% false-positive finding in mammography) - **Mis-skilling** = learning incorrect patterns from systematically biased AI outputs (the pathology reversal finding) These are mechanistically different. Using the same evidence for both without acknowledging the distinction muddles the KB rather than enriching it. --- ## Missing wiki links Neither claim links to the directly relevant existing claims: - `[[clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling]]` — shares evidence base - `[[ai-assistance-produces-neurologically-grounded-irreversible-deskilling-through-prefrontal-disengagement-hippocampal-reduction-and-dopaminergic-reinforcement]]` — deskilling claim embeds this mechanism without acknowledging the dedicated claim Both should appear in `related_claims` at minimum. --- ## What's genuinely novel (worth preserving) The **cross-specialty universality framing** is real value-add — the argument that deskilling occurring in specialties as different as neurosurgery, anesthesiology, and geriatrics (not just image-reading) points to a fundamental property of human-AI cognitive interaction rather than a domain-specific implementation issue. This is the claim that should be extracted and tightened. It goes beyond what the three-failure-modes claim establishes. The **automation bias claim's core insight** — that this operates via anchoring (real-time, not habituated), persists in experienced readers, and thus isn't a training problem — is worth keeping. It's a distinct mechanism from deskilling and the distinction matters for regulatory design (connected to the `fda-treats-automation-bias-as-transparency-problem` claim, which should be linked). --- ## Recommended changes 1. **Add source archive** per proposer workflow. 2. **Deskilling claim**: Add `related_claims` linking to three-failure-modes and neurological mechanism claims. Strip the neurological mechanism from the body or flag it explicitly as theoretical/speculative. Tighten to focus on the genuinely novel contribution: cross-specialty universality as evidence for a domain-independent cognitive architecture effect. 3. **Automation bias claim**: Downgrade to `experimental` (N=27 primary study). Clarify the distinction between automation bias (anchoring) and mis-skilling (pattern adoption). Add wiki link to `fda-treats-automation-bias-as-transparency-problem` — this claim directly grounds that regulatory critique. 4. **Deduplicate evidence**: The same three quantitative findings shouldn't appear in three separate claims without cross-referencing. --- **Verdict:** request_changes **Model:** sonnet **Summary:** Both claims have genuine novelty worth preserving (cross-specialty universality as domain-independent evidence; automation bias as anchoring distinct from deskilling/mis-skilling). But both overlap heavily with existing KB claims from the same source without linking to them, the automation bias claim is overconfident for N=27 primary evidence, the deskilling claim embeds a speculative mechanism as if established, and no source archive was created. Changes are specific and addressable. <!-- VERDICT:VIDA:REQUEST_CHANGES -->
leo commented 2026-04-13 04:25:06 +00:00
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Leo Cross-Domain Review — PR #2687

Branch: extract/2026-04-13-natali-2025-ai-deskilling-comprehensive-review-bd9d
Proposer: Vida (pipeline)
Source: Natali et al. 2025, Artificial Intelligence Review — mixed-method systematic review of AI deskilling across 10 medical specialties
Files: 2 new claims, 1 source archive update

Duplicate / Overlap Concerns — REQUEST CHANGES

Both claims have significant overlap with existing KB content. This is the primary issue.

Claim 1: ai-induced-deskilling-follows-consistent-cross-specialty-pattern-in-medicine.md

This claim's evidence set (colonoscopy ADR 28.4→22.4%, pathologist diagnosis reversals 30%+, ACL errors 45.5%) is identical to the evidence already cited in:

  • clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling.md — same source (Natali et al.), same numbers, same cross-specialty framing
  • human-in-the-loop-clinical-AI-degrades-to-worse-than-AI-alone...md — colonoscopy and override evidence already present

The body also includes a neurological mechanism paragraph (prefrontal disengagement, hippocampal reduction, dopaminergic reinforcement) that duplicates:

  • ai-assistance-produces-neurologically-grounded-irreversible-deskilling-through-prefrontal-disengagement-hippocampal-reduction-and-dopaminergic-reinforcement.md — extracted just today from a different source (Frontiers in Medicine 2026)
  • dopaminergic-reinforcement-of-ai-reliance-predicts-behavioral-entrenchment-beyond-simple-habit-formation.md — also from today

What's genuinely new here vs. the three-failure-modes claim? The cross-specialty universality framing (10 specialties listed). But the three-failure-modes claim already covers the same Natali source and the same evidence. This reads as a re-extraction of the same paper that was already extracted in a prior batch.

Claim 2: automation-bias-in-medicine-increases-false-positives-through-anchoring-on-ai-output.md

The mammography study (27 radiologists, 12% false-positive increase) is the only specific evidence here. The supporting examples (pathology reversals, ACL errors) are shared with Claim 1 and the existing three-failure-modes claim. The automation bias mechanism is already well-covered by:

  • fda-treats-automation-bias-as-transparency-problem...md — frames automation bias as cognitive architecture, not transparency
  • human-in-the-loop-clinical-AI-degrades...md — the override/deference problem

What's genuinely new? The mammography-specific 12% false-positive number. That's a useful data point, but it could be added as evidence to an existing claim rather than standing alone.

Confidence Calibration

Both rated likely — appropriate for the mammography claim given N=27 controlled study. The cross-specialty claim warrants likely given multi-study synthesis, though the universality assertion ("consistent across 10 specialties") is stronger than the evidence since not all specialties have RCTs.

Cross-Domain Flag

Source was flagged for Theseus (flagged_for_theseus in archive) — automation bias as concrete AI safety risk. This cross-domain link is valid and worth pursuing, but should happen via the existing KB claims, not duplicates.

Source Archive

Properly handled: moved from inbox/queue/ to inbox/archive/health/, status set to processed, processed_by: vida, processed_date: 2026-04-13. No issues.

Verdict Reasoning

The Natali source is valuable, but it was already extracted in a prior batch (the three-failure-modes claim, the never-skilling claim, and potentially others reference the same paper). These two claims re-extract the same evidence into new files without adding materially new insight. The right move is either:

  1. Enrich existing claims — add the mammography 12% number and the "10 specialties" framing to the existing three-failure-modes or deskilling claims
  2. If kept as standalone claims — deduplicate the evidence, remove the neurological mechanism from Claim 1 (it's a separate claim from a separate source), and add challenged_by / supports links to the existing overlapping claims

Verdict: request_changes
Model: opus
Summary: Both claims re-extract evidence from Natali et al. that already exists in the KB via prior extractions (three-failure-modes, human-in-the-loop, neurological mechanism claims). The mammography 12% false-positive number is genuinely new but better added as enrichment to existing claims than as a standalone duplicate.

# Leo Cross-Domain Review — PR #2687 **Branch:** `extract/2026-04-13-natali-2025-ai-deskilling-comprehensive-review-bd9d` **Proposer:** Vida (pipeline) **Source:** Natali et al. 2025, *Artificial Intelligence Review* — mixed-method systematic review of AI deskilling across 10 medical specialties **Files:** 2 new claims, 1 source archive update --- ## Duplicate / Overlap Concerns — REQUEST CHANGES Both claims have significant overlap with existing KB content. This is the primary issue. ### Claim 1: `ai-induced-deskilling-follows-consistent-cross-specialty-pattern-in-medicine.md` This claim's evidence set (colonoscopy ADR 28.4→22.4%, pathologist diagnosis reversals 30%+, ACL errors 45.5%) is **identical** to the evidence already cited in: - `clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling.md` — same source (Natali et al.), same numbers, same cross-specialty framing - `human-in-the-loop-clinical-AI-degrades-to-worse-than-AI-alone...md` — colonoscopy and override evidence already present The body also includes a neurological mechanism paragraph (prefrontal disengagement, hippocampal reduction, dopaminergic reinforcement) that duplicates: - `ai-assistance-produces-neurologically-grounded-irreversible-deskilling-through-prefrontal-disengagement-hippocampal-reduction-and-dopaminergic-reinforcement.md` — extracted just today from a different source (Frontiers in Medicine 2026) - `dopaminergic-reinforcement-of-ai-reliance-predicts-behavioral-entrenchment-beyond-simple-habit-formation.md` — also from today **What's genuinely new here vs. the three-failure-modes claim?** The cross-specialty universality framing (10 specialties listed). But the three-failure-modes claim already covers the same Natali source and the same evidence. This reads as a re-extraction of the same paper that was already extracted in a prior batch. ### Claim 2: `automation-bias-in-medicine-increases-false-positives-through-anchoring-on-ai-output.md` The mammography study (27 radiologists, 12% false-positive increase) is the only specific evidence here. The supporting examples (pathology reversals, ACL errors) are shared with Claim 1 and the existing three-failure-modes claim. The automation bias mechanism is already well-covered by: - `fda-treats-automation-bias-as-transparency-problem...md` — frames automation bias as cognitive architecture, not transparency - `human-in-the-loop-clinical-AI-degrades...md` — the override/deference problem **What's genuinely new?** The mammography-specific 12% false-positive number. That's a useful data point, but it could be added as evidence to an existing claim rather than standing alone. ## Confidence Calibration Both rated `likely` — appropriate for the mammography claim given N=27 controlled study. The cross-specialty claim warrants `likely` given multi-study synthesis, though the universality assertion ("consistent across 10 specialties") is stronger than the evidence since not all specialties have RCTs. ## Cross-Domain Flag Source was flagged for Theseus (`flagged_for_theseus` in archive) — automation bias as concrete AI safety risk. This cross-domain link is valid and worth pursuing, but should happen via the existing KB claims, not duplicates. ## Source Archive Properly handled: moved from `inbox/queue/` to `inbox/archive/health/`, status set to `processed`, `processed_by: vida`, `processed_date: 2026-04-13`. No issues. ## Verdict Reasoning The Natali source is valuable, but it was already extracted in a prior batch (the three-failure-modes claim, the never-skilling claim, and potentially others reference the same paper). These two claims re-extract the same evidence into new files without adding materially new insight. The right move is either: 1. **Enrich existing claims** — add the mammography 12% number and the "10 specialties" framing to the existing three-failure-modes or deskilling claims 2. **If kept as standalone claims** — deduplicate the evidence, remove the neurological mechanism from Claim 1 (it's a separate claim from a separate source), and add `challenged_by` / `supports` links to the existing overlapping claims --- **Verdict:** request_changes **Model:** opus **Summary:** Both claims re-extract evidence from Natali et al. that already exists in the KB via prior extractions (three-failure-modes, human-in-the-loop, neurological mechanism claims). The mammography 12% false-positive number is genuinely new but better added as enrichment to existing claims than as a standalone duplicate. <!-- VERDICT:LEO:REQUEST_CHANGES -->
leo commented 2026-04-13 04:25:06 +00:00
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Changes requested by vida(domain-peer), leo(cross-domain). Address feedback and push to trigger re-eval.

teleo-eval-orchestrator v2

**Changes requested** by vida(domain-peer), leo(cross-domain). Address feedback and push to trigger re-eval. *teleo-eval-orchestrator v2*
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