271 Interrater and Intrarater Analysis of Ultrasound and Histological Findings in Patients with Suspected Giant Cell Arteritis
Bibliographic record
Abstract
Background: US is emerging as an alternative test to performing a temporal artery biopsy in the diagnosis of GCA. Little is known of the variability in interpretation of these tests by sonographers and pathologists. We undertook an interobserver analysis to assess agreement between sonographers in interpreting US videos and between pathologists for biopsy images in patients with suspected GCA. Methods: We developed a web exercise with 30 cases randomly sampled from patients with suspected GCA recruited to a large multicentre study comparing US with biopsy for the diagnosis of GCA. We used 5 practice cases, followed by the 30 unique cases and 6 interspersed repeats, showing US videos of both temporal arteries and high-quality scanned images of biopsies. Trained sonographers and pathologists from the study were asked to assess the compatibility of the videos and images with a diagnosis of GCA and indicate how confident they were of the diagnosis. Interobserver agreement between sonographers and between pathologists was evaluated using two-way random effects analysis of variance to estimate the intraclass correlation coefficient (ICC) for agreement. Intra-observer reproducibility was evaluated using κ statistics for the six repeated cases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.039 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".