Males with Kawasaki disease develop coronary artery aneurysms more than twice as much as females
Bibliographic record
Abstract
Objectives: Kawasaki disease (KD) is the leading cause of acquired childhood coronary aneurysms (CAA). Males are more affected than females, with lower survival from cardiac events and normalization rates. This study aimed to determine the association between biological sex and CAA risk and evaluate the association with baseline biochemical inflammatory markers by biological sex. Methods: This multicenter retrospective cohort study involved children ≤10 years old diagnosed with KD in five Canadian centres. Adjusted CAA risk differences between sexes were computed using binomial regression. Associations between inflammatory markers and CAA risk were analyzed using logistic regression with interaction terms between sex and inflammatory markers. Results: From 2004 to 2015, 1382 patients were diagnosed with KD and 812 (59%) were males. Median age, fever total duration, and fever duration at therapy initiation were similar between the sexes. The cumulative incidence of medium to large (Z ≥ 5) CAA was higher in males [70/812 (8.6%)] compared to females [19/570 (3.3%)], with an adjusted risk difference of 4.6 % (95% confidence interval [CI] 2.1 to 7.1). Large (Z > 10) aneurysms were more prevalent in males (adjusted risk difference of 3.3%, 95% CI 1.7 to 5.0). Most inflammatory markers were positively associated with CAA risk, but the association was not statistically different between sexes. Conclusion: Males with KD are at higher risk of developing CAA compared to females. The majority of patients were presumed to be prepubertal, suggesting that hormonal influences are unlikely to be a significant factor. Future KD research based on biological sex categorization should focus on patient risk stratification and long-term prognostic evaluation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".