Abstract 173: Coronary artery aneurysm measurement and z-score variability in Kawasaki Disease
Bibliographic record
Abstract
Background: Coronary artery (CA) z-scores are commonly used for clinical decisions in Kawasaki disease (KD). We evaluated reliability in CA measurement, reproducibility of z-score calculation, and frequency with which different z-score formulas lead to divergent management strategies. Methods: We randomly selected 21 KD patients (pts) with ≥1 CA z-score 1.5-3 and all KD pts with ≥ 1 CA z-score 7-14 (n=20). Two echocardiographers measured LMCA, LAD and RCA. Inter- and intraobserver reliability were calculated. T-tests were used to compare CA z-score using 3 commonly used formulas (Boston, DC and Montreal). Results: Median age at KD echo was 1.2 y (0.2-11.5 y). Interobserver reliability was high for LAD (intraclass correlation [ICC] 0.970) and RCA (ICC 0.943) and lower for LMCA (ICC 0.725). Intraobserver reliability was also high for LAD and RCA (ICC 0.991 and 0.999) and lower for LMCA (ICC 0.946). Z-scores for the 3 formulas were similar at smaller CA size, i.e., z < 3, but varied markedly at larger CA dimensions (Figure). Z-scores for the same CA dimension calculated by each of the 3 formulas resulted in disparate classification of normal vs. mild dilation in 7/21 (22%) pts, and different guidance for anticoagulation based on CA z ≥10 in 10/20 (50%) pts. Conclusion: Although CA measurements have high inter- and intraobserver agreement, CA z-scores vary dramatically based on the z-score formula, particularly at larger CA dimensions. Discrepancies in CA z-score between calculators impacts not only the distinction between normal and mild dilatation, but most importantly, the recommendation of anticoagulation for pts with larger CA dimensions.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".