Educating Healthcare Professionals and The Public on Kawasaki Disease in Canada: A Scoping Review
Bibliographic record
Abstract
Background: Kawasaki disease (KD) is an inflammatory condition that impacts the walls of blood vessels, potentially leading to acquired heart disease. Notably, Canada exhibits the highest incidence of KD among children below the age of 5 from the Western countries. Early treatment with intravenous immunoglobulin (IVIG) is effective in preventing coronary artery issues, but untreated cases can result in coronary artery aneurysms, which can cause severe heart conditions. Aim/Objective: The purpose of this scoping review is to understand which populations are more susceptible to KD, available screening options and long-term patient support, and educational resources provided to patients, families, and healthcare providers. Methods: Articles from PubMed and Google Scholar databases were assessed between January 2000 and November 2023. After applying the inclusion and exclusion criteria, 34 articles were extracted. Results: Our findings indicate a higher KD prevalence among young children and individuals of East Asian descent. The literature highlighted a need for improved public and healthcare provider education, particularly regarding parental advocacy and sharing of KD diagnostic guidelines by physicians. There is also a gap in the long-term support for KD patients, particularly after disease treatment. Discussion: The need for an improved understanding of diagnostic criteria and the development of digital educational resources for KD research was emphasized. Conclusion: Public and professional education, combined with long-term patient support, can improve outcomes for KD patients and alleviate the burden on the Canadian healthcare system.
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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 | 0.012 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.024 | 0.036 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".