Abstract 13322: Association Between Allergic Diseases and Development of Kawasaki Disease Among Children: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Introduction: Kawasaki disease (KD) is an acute systemic vasculitis of unknown etiology. It is the leading cause of pediatric acquired heart disease in developed countries. Growing evidence points to the role of immunoregulatory abnormalities for its causation. Hypothesis: It is hypothesized that a history of allergic diseases is associated with the development of KD in children. Methods: A systematic review and meta-analysis of observational studies was performed to ascertain the association between history of allergic diseases and KD. Major electronic databases were searched from inception through March 2020 using pre-defined index terms such as “allergic disease”, “Kawasaki disease” and “children”. Random effects model was used to derive pooled estimates of odds ratios (OR) and 95% confidence intervals (CI). The Newcastle-Ottawa Scale was used for qualitative assessment. This study was reported according to MOOSE (Meta-analyses Of Observational Studies in Epidemiology). Results: Of 854 articles identified, 8 studies were considered eligible. History of allergic diseases was significantly associated with the development of KD (OR 1.99; 95% CI 1.26-2.73). On subgroup analysis of case-control studies (n=6), allergic diseases remained significantly associated with KD (OR 1.61; 95% CI 1.10-2.12). However, subgroup analysis with allergic diseases limited to asthma (n=5) revealed no significant association with KD (OR 1.17; 95% CI 0.45-1.90). Qualitative assessment found minimal concerns for bias. Conclusions: The available evidence suggests a strong association between history of allergic diseases and development of Kawasaki disease among children.
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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".