Abstract O.62: Estimation Of The Severity Of Coronary Artery Aneurysm By Z-score Of The Internal Diameter In Kawasaki Disease.
Bibliographic record
Abstract
Background: The standard values of normal coronary artery internal diameters in Japanese children have been recently established, making it possible to calculate Z-scores based on body surface area. The aim of this study was to clarify the appropriate cut-off points of coronary artery aneurysm (CAA) Z-scores to predict coronary events such as stenosis, obstruction, and thrombosis in patients with Kawasaki disease (KD). Methods: In this multicenter retrospective study, we investigated height, weight, CAA diameters measured by echocardiography in acute phase KD, and coronary events in CAA patients with KD (age 18 years or younger) who had coronary angiography from 1992 to 2011. Results: Interim analysis was performed on data of the 928 patients recruited from 45 institutions. Body surface area (calculated from height and weight) and CAA diameters were available in 702, 680, and 539 cases of right coronary artery (RCA), left main trunk (LMT), left anterior descending artery (LAD), respectively. Coronary events occurred in 62 RCA cases (8.8%), 8 LMT cases (1.2%), and 45 LAD cases (8.3%) . Areas under the ROC curves to predict coronary events were similar for actual diameter, Z-score, and the ratio of actual diameter to that showing a Z-score of zero in each segment. The cut-off points for the actual diameter, Z-score, and ratio which yielding the highest sensitivity plus specificity were 6.3 mm, 9.6, and 3.9 times for RCA; 7.4 mm, 11.1, and 2.8 times for LMT; and 5.3 mm, 8.9, and 3.5 times for LAD. Conclusions: We identified cut-off Z-scores for CAA diameters useful for coronary events prediction. Attention should be paid to coronary events when the Z-score for CAA diameter is over 10.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".