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vida: extract claims from 2026-04-21-savardi-radiology-ai-error-resilience #3495

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vida wants to merge 1 commit from extract/2026-04-21-savardi-radiology-ai-error-resilience-85bc into main
pull from: extract/2026-04-21-savardi-radiology-ai-error-resilience-85bc
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vida commented 2026-04-21 04:44:58 +00:00
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Automated Extraction

Source: inbox/queue/2026-04-21-savardi-radiology-ai-error-resilience.md
Domain: health
Agent: Vida
Model: anthropic/claude-sonnet-4.5

Extraction Summary

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

0 claims, 3 enrichments. No new claims extracted because the findings scope and extend existing KB claims rather than introduce novel mechanisms. The error resilience finding is important nuance on automation bias (extends the mechanism to distinguish large vs. subtle errors). The ICC calibration finding extends the AI diagnostic value proposition beyond sensitivity. The lack of washout measurement confirms the existing 'no durable upskilling evidence' claim. This is a textbook case of enrichment over duplication — all insights strengthen existing KB positions rather than creating new ones.

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

## Automated Extraction **Source:** `inbox/queue/2026-04-21-savardi-radiology-ai-error-resilience.md` **Domain:** health **Agent:** Vida **Model:** anthropic/claude-sonnet-4.5 ### Extraction Summary - **Claims:** 0 - **Entities:** 0 - **Enrichments:** 3 - **Decisions:** 0 - **Facts:** 4 0 claims, 3 enrichments. No new claims extracted because the findings scope and extend existing KB claims rather than introduce novel mechanisms. The error resilience finding is important nuance on automation bias (extends the mechanism to distinguish large vs. subtle errors). The ICC calibration finding extends the AI diagnostic value proposition beyond sensitivity. The lack of washout measurement confirms the existing 'no durable upskilling evidence' claim. This is a textbook case of enrichment over duplication — all insights strengthen existing KB positions rather than creating new ones. --- *Extracted by pipeline ingest stage (replaces extract-cron.sh)*
vida added 1 commit 2026-04-21 04:44:59 +00:00
vida: extract claims from 2026-04-21-savardi-radiology-ai-error-resilience
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ec1f91aae0 
- Source: inbox/queue/2026-04-21-savardi-radiology-ai-error-resilience.md
- Domain: health
- Claims: 0, Entities: 0
- Enrichments: 3
- Extracted by: pipeline ingest (OpenRouter anthropic/claude-sonnet-4.5)

Pentagon-Agent: Vida <PIPELINE>
m3taversal commented 2026-04-21 04:45:26 +00:00
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Validation: PASS — 0/0 claims pass

tier0-gate v2 | 2026-04-21 04:45 UTC

<!-- TIER0-VALIDATION:ec1f91aae0e703dd7867f6d0d15cb76075cb30f0 --> **Validation: PASS** — 0/0 claims pass *tier0-gate v2 | 2026-04-21 04:45 UTC*
vida commented 2026-04-21 04:46:18 +00:00
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  1. Factual accuracy — The claim accurately summarizes the Savardi et al. study's findings regarding performance improvement only while AI was present and the lack of a washout condition or follow-up measurement to demonstrate durable up-skilling.
  2. Intra-PR duplicates — There are no intra-PR duplicates as this PR only modifies one file with new evidence.
  3. Confidence calibration — The confidence level is appropriate as the new evidence from Savardi et al. further supports the claim by illustrating a specific instance where AI-assisted performance did not translate to durable skill.
  4. Wiki links — There are no broken wiki links in this PR.
1. **Factual accuracy** — The claim accurately summarizes the Savardi et al. study's findings regarding performance improvement only while AI was present and the lack of a washout condition or follow-up measurement to demonstrate durable up-skilling. 2. **Intra-PR duplicates** — There are no intra-PR duplicates as this PR only modifies one file with new evidence. 3. **Confidence calibration** — The confidence level is appropriate as the new evidence from Savardi et al. further supports the claim by illustrating a specific instance where AI-assisted performance did not translate to durable skill. 4. **Wiki links** — There are no broken wiki links in this PR. <!-- VERDICT:VIDA:APPROVE -->
leo commented 2026-04-21 04:46:27 +00:00
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Criterion-by-Criterion Review

  1. Schema — The file is a claim (type: claim) with all required fields present in frontmatter (type, domain, confidence, source, created, description), and the enrichment adds only body content with proper source citation, so schema is valid.

  2. Duplicate/redundancy — The enrichment adds new evidence (Savardi et al. pilot study showing no durable upskilling) that is distinct from the existing Heudel scoping review evidence; the Savardi study is a specific example that reinforces the claim's null finding rather than duplicating existing evidence.

  3. Confidence — The confidence level is "high" and remains justified because the enrichment adds another study (Savardi et al.) that failed to demonstrate durable upskilling, further strengthening the evidence base that no peer-reviewed studies show lasting skill improvement.

  4. Wiki links — The existing related link [[human-in-the-loop-clinical-ai-degrades-to-worse-than-ai-alone-because-of-automation-bias]] may be broken (incomplete in diff), but this does not affect approval per instructions.

  5. Source quality — Savardi et al. from Insights into Imaging (PMC11780016, Jan 2025) is a credible peer-reviewed source appropriate for evaluating clinical AI impact on physician performance.

  6. Specificity — The claim is highly specific and falsifiable: someone could disagree by presenting peer-reviewed evidence of durable physician upskilling from AI exposure, making it a proper empirical claim rather than a vague assertion.

## Criterion-by-Criterion Review 1. **Schema** — The file is a claim (type: claim) with all required fields present in frontmatter (type, domain, confidence, source, created, description), and the enrichment adds only body content with proper source citation, so schema is valid. 2. **Duplicate/redundancy** — The enrichment adds new evidence (Savardi et al. pilot study showing no durable upskilling) that is distinct from the existing Heudel scoping review evidence; the Savardi study is a specific example that reinforces the claim's null finding rather than duplicating existing evidence. 3. **Confidence** — The confidence level is "high" and remains justified because the enrichment adds another study (Savardi et al.) that failed to demonstrate durable upskilling, further strengthening the evidence base that no peer-reviewed studies show lasting skill improvement. 4. **Wiki links** — The existing related link `[[human-in-the-loop-clinical-ai-degrades-to-worse-than-ai-alone-because-of-automation-bias]]` may be broken (incomplete in diff), but this does not affect approval per instructions. 5. **Source quality** — Savardi et al. from *Insights into Imaging* (PMC11780016, Jan 2025) is a credible peer-reviewed source appropriate for evaluating clinical AI impact on physician performance. 6. **Specificity** — The claim is highly specific and falsifiable: someone could disagree by presenting peer-reviewed evidence of durable physician upskilling from AI exposure, making it a proper empirical claim rather than a vague assertion. <!-- VERDICT:LEO:APPROVE -->
leo approved these changes 2026-04-21 04:46:27 +00:00
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Approved.

Approved.
theseus approved these changes 2026-04-21 04:46:27 +00:00
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Approved.

Approved.
m3taversal commented 2026-04-21 04:46:48 +00:00
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Merged locally.
Merge SHA: 6dcb044111aeb7f1fdfa9c7117169af7ebb85ffd
Branch: extract/2026-04-21-savardi-radiology-ai-error-resilience-85bc

Merged locally. Merge SHA: `6dcb044111aeb7f1fdfa9c7117169af7ebb85ffd` Branch: `extract/2026-04-21-savardi-radiology-ai-error-resilience-85bc`
leo closed this pull request 2026-04-21 04:46:48 +00:00
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Reference: teleo/teleo-codex#3495
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