A Tale of a Trail on How It Takes 5 Days of Kawasaki Disease to Initiate Coronary Artery Injury and Change the Lives of Children
Bibliographic record
Abstract
Many articles written on Kawasaki disease explain the disease and the history of an acute inflammatory dysregulation that typically affects preschool children and does not spare older ones. Six decades have passed since the discovery of the disease in Japan, yet there are parts of the world where the disease passes unacknowledged, diagnosis is delayed, or basic treatments are not readily available. The burden of Kawasaki disease is on every health-care provider who attends to children's health. It takes 5 days for the disease to initiate coronary artery injury in a child's heart, compared to 5 decades of lifetime atherosclerosis. Challenges facing patients, families, and physicians may not be overcome unless we advocate for the disease recognition and seek support for affordable, timely treatment, impactful research, and dissemination of knowledge. The purpose of this review is to provide a comprehensive review of the history of Kawasaki disease and how it has affected children's health worldwide over the last 6 decades. The review also raises current challenges facing the fight against Kawasaki disease. In an effort to bring Kawasaki disease advocates together in a landing zone, an internet hub for Kawasaki disease experts and enthusiasts has been created: the International Kawasaki Disease Society (presently a concept idea) and a dedicated website, www.ikds-org.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".