Claims

The LLM judge agrees with human annotators at 87.7% (κ = 0.842) on consensus pairs, but severity-rating agreement is uneven and medium-tier boundaries are uncertain

Claim · All claims
Evidence · 1 study (2 entries) · associational r2 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person · recheck by 2029: tool and model accuracy changes quickly

Subclaims

Reading the evidence codes

q is the design tier, i the impact, n the sample, and the kind with its r the rigour judged within that kind: what each code means.

This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.

q2 i? On the 146 pairs where both annotators agree, judge agreement is 87.7% (κ = 0.842; macro-F1 0.841). → Yubo Li 2026 q2 i? Severity agreement is less settled: judge agreement is 65.3% with annotator A (κ = 0.385) but 93.9% with annotator B (κ = 0.827), with medium-tier boundaries particularly uncertain. → Yubo Li 2026 (2)

Evidence

Yubo Li 2026

Yubo Li, Rema Padman, Ramayya Krishnan. (2026). Auditing Institutional Heterogeneity for Generative AI in Patient Education: A Large-Scale Study of 102 US Transplant Handbooks. arXiv preprint. https://arxiv.org/abs/2607.22606 doi:10.48550/arxiv.2607.22606

q2 · i? · associational · r2

Human-annotation study of 200 pairs stratified 40 per judge label, independently labelled by two annotators with judge outputs hidden. Inter-annotator agreement is 73.0% (κ = 0.655); the article notes the subset score is unsuitable as population accuracy.

"On the 146 pairs where A and B agree, judge agreement is 87.7% (κ= 0.842 ; macro-F1 0.841). Per-label F1 on this subset is 1.00 for ABSENT, 0.83 CONSISTENT, 0.70 COMPLEMENTARY, 0.69 DIVERGENT, and 0.99 CONTRADICTORY."

Yubo Li 2026 (2)

Yubo Li, Rema Padman, Ramayya Krishnan. (2026). Auditing Institutional Heterogeneity for Generative AI in Patient Education: A Large-Scale Study of 102 US Transplant Handbooks. arXiv preprint. https://arxiv.org/abs/2607.22606

q2 · i? · associational · r2

Severity-calibration subset of the same 200-pair annotation study, summarized in Table 1 showing medium-tier matches are particularly uncertain. The article therefore uses severity only as a review flag, without treating either annotator as an adjudicated clinical reference.

"Among 49 pairs with shared relationship labels and three available severity ratings, A–B agreement is 67.3% ( κ= 0.425 ); judge agreement is 65.3% with A (κ= 0.385 ) and 93.9% with B (κ= 0.827 )."

Discussion

Page metadata
Field Value
type claim
id judge-calibration-labels-strong-severity-uneven
title The LLM judge agrees with human annotators at 87.7% (κ = 0.842) on consensus pairs, but severity-rating agreement is uneven and medium-tier boundaries are uncertain
description The LLM judge agrees with human annotators at 87.7% (κ = 0.842) on consensus pairs, but severity-rating agreement is uneven and medium-tier boundaries are uncertain
status review
evidence_strength mixed
newest_evidence 2026
recheck_by 2029
generated by process:wiki-ingest on 2026-10-10
trust unverified — no one has confirmed this page's content
sources 2 — yubo-li-2026, yubo-li-2026-2

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