Repeated systematic surveillance of Kawasaki disease in Ontario from 1995 to 2006
Bibliographic record
Abstract
BACKGROUND: Rising incidences of Kawasaki disease (KD) have been reported worldwide. Reported herein are the results of 4 triennial KD surveillances conducted in Ontario. METHODS: Between 1995 and 2006 all hospitals in Ontario were asked on 4 occasions to identify all patients with discharge diagnoses of KD and report incident cases. RESULTS: The latest surveillance identified 697 new KD patients (100% response rate) for a total of 2378 KD patients through all 4 surveillances. Yearly incidence was 26.2/100,000 for <5 years old, 6.7/100,000 for 5-9 years old and 0.9/100,000 for 10-14 years old. KD incidence significantly increased from 1995 to 2006, although the increase seemed to plateau between the 3rd and 4th surveillance. There was an increase in the proportion of patients diagnosed with incomplete KD and a significant reduction in the rate of coronary artery abnormalities, possibly due to better disease recognition and treatment. Hospitals reporting <20 cases per surveillance were found to be more likely to report cases with incomplete KD. These patients were also less likely to be treated with i.v. immunoglobulin and aspirin but were more likely to be treated with antibiotics, suggesting uncertainties regarding diagnosis and management of KD patients in those centers. CONCLUSIONS: The incidence of KD in Ontario is possibly one of the highest outside of Asia and has been rising since 1995. Although the most recent surveillance demonstrated improved cardiac outcomes, treatment delays or absence thereof continue to be a problem. Effective diagnosis and prompt treatment remain critical aspects of KD management.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".