Unexpectedly high incidence of Kawasaki Disease in a Canadian Atlantic Province- an 11-year retrospective descriptive study
Bibliographic record
Abstract
BACKGROUND: Kawasaki Disease (KD) is the leading cause of acquired heart disease in children in developed countries with a variable incidence worldwide. Previous studies reported an unexpectedly high incidence of KD in the Canadian Atlantic Provinces. The goals of our study were to validate this finding in the province of Nova Scotia and to carefully review patients' characteristics and disease outcomes. METHODS: This was a retrospective review of all children < 16 years old from Nova Scotia diagnosed with KD between 2007-2018. Cases were identified using a combination of administrative and clinical databases. Clinical information was collected retrospectively by health record review using a standardized form. RESULTS: Between 2007-2018, 220 patients were diagnosed with KD; 61.4% and 23.2% met the criteria for complete and incomplete disease, respectively. The annual incidence was 29.6 per 100,000 children < 5 years. The male to female ratio was 1.3:1 and the median age was 3.6 years. All patients diagnosed with KD in the acute phase received intravenous immunoglobulin (IVIG); 23 (12%) were refractory to the first dose. Coronary artery aneurysms were found in 13 (6%) patients and one patient died with multiple giant aneurysms. CONCLUSION: We have confirmed an incidence of KD in our population which is higher than that reported in Europe and other regions of North America despite our small Asian population. The comprehensive method to capture patients may have contributed to the detection of the higher incidence. The role of local environmental and genetic factors also deserves further study. Increased attention to regional differences in the epidemiology of KD may improve our understanding of this important childhood vasculitis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 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) | 0.001 | 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".