Rate, associated factors and outcomes of recurrence of Kawasaki disease in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Previous studies on recurrence of Kawasaki disease (KD) have mostly been limited to Japan, which has an incidence of KD 8-10-fold higher than North America. The aim of the present study was to determine the rate of KD recurrence for patients in Ontario, to identify factors potentially associated with increased odds of recurrence, and to compare the clinical course and outcomes of index and recurrent KD episodes. METHODS: Review was undertaken of all patients with recurrence of KD identified in Ontario, Canada, from 1995 to 2006. All patients with recurrence of KD (defined as at least three clinical signs of KD in addition to fever ≥ 5 days), presenting ≥ 14 days after the return to baseline from the index episode were included. RESULTS: A total of 1010 patients were followed for 5786 patient-years. During this period a total of 17 recurrent episodes in 16 patients were identified at a median of 1.5 years after the initial episode (2 weeks-5 years). Rate of recurrence of KD was 2.9 episodes/1000 patient-years, which is higher than the expected annual incidence of KD in the same age group (26.2/100,000 per year). No factors associated with increased risk of recurrence were identified, perhaps due to the small number of events. Clinical course and outcomes of the index and recurrent KD episodes were similar. CONCLUSIONS: A previous history of KD should increase the index of suspicion for future episodes of KD to allow for rapid recognition, treatment and to achieve optimal outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".