Delivery Type and Other Birth Factors Associated With Kawasaki Disease
Bibliographic record
Abstract
BACKGROUND: Kawasaki disease (KD) onset has been suggested to be associated with infections and various environmental factors. However, research on whether the delivery type plays a role in KD development is limited. This study investigated whether cesarean section (CS) or vaginal delivery (VD) is associated with KD onset using a large administrative claims database in Japan. METHOD: We conducted a case-control study using the JMDC Claims Database from January 2005 to December 2021. Data on children born via CS or VD and their mothers were collected. KD patients were identified from the source population, and controls without KD were randomly selected based on sex, age and registration time, each matched to 4 controls using a risk-set sampling technique. We analyzed the association between delivery type and KD onset using multivariate conditional logistic regression, defining KD as the primary outcome based on specific criteria. RESULTS: Case-control matching created 3363 pairs of cases (n = 3363) and controls (n = 13,363). The proportions of CS, maternal age, Charlson Comorbidity Index, presence of older siblings and low birth weight infants were significantly different between the cases and controls. In the multivariate analysis, KD onset was associated with CS [odds ratio (OR): 1.12; 95% confidence interval (CI): 1.02-1.24], the presence of older siblings (OR: 1.11; 95% CI: 1.02-1.21), lower birth weight (1001-2500 g) (OR: 1.22; 95% CI: 1.04-1.43) and antibiotic use (OR: 1.12; 95% CI: 1.02-1.24). CONCLUSIONS: The risk of developing KD may be influenced by the delivery type (CS or VD), the presence of older siblings, low birth weight and antibiotic use.
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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 | 0.001 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".