Seasons of Kawasaki Disease during the COVID-19 pandemic
Bibliographic record
Abstract
The incidence of Kawasaki Disease has a peak in the winter months with a trough in late summer/early fall. Environmental/exposure factors have been associated with a time-varying incidence. These factors were altered during the COVID-19 pandemic. The study was performed through the International Kawasaki Disease Registry. Data from patients diagnosed with acute Kawasaki Disease and Multiple Inflammatory Syndrome-Children were obtained. Guideline case definitions were used to confirm site diagnosis. Enrollment was from 1/2020 to 7/2023. The number of patients was plotted over time. The patients/month were tabulated for the anticipated peak Kawasaki Disease season (December-April) and non-peak season (May-November). Data were available for 1975 patients from 11 large North American sites with verified complete data and uninterrupted site reporting. The diagnosis criteria were met for 531 Kawasaki Disease and 907 Multiple Inflammatory Syndrome-Children patients. For Multiple Inflammatory Syndrome-Children there were peaks in January of 2021 and 2022. For Kawasaki Disease, 2020 began (January-March) with a seasonal peak (peak 26, mean 21) with a subsequent fall in the number of cases/month (mean 11). After the onset of the pandemic (April 2020), there was no clear seasonal Kawasaki Disease variation (December-April mean 12 cases/month and May-November mean 10 cases/month). During the pandemic, the prevalence of Kawasaki Disease decreased and the usual seasonality was abolished. This may represent the impact of pandemic public health measures in altering environmental/exposure aetiologic factors contributing to the incidence of Kawasaki Disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 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".