The Effectiveness of No or Low-Dose versus High-Dose Aspirin in Treating Acute Kawasaki Disease: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
This systematic review and meta-analysis assesses the effectiveness of no or low-dose versus high-dose aspirin on the incidence of coronary artery aneurysms (CAAs), intravenous immunoglobulin (IVIG) resistance, hospital stay length, and fever duration during the acute phase of Kawasaki disease. Our review adheres to the Preferred Reporting Items for Systematic Reviews guidelines. The PubMed and Google Scholar databases were comprehensively searched to identify relevant studies in the literature, including observational studies and randomized controlled trials (RCTs). The primary outcome was the incidence of CAAs. The secondary outcomes were the hospital stay length, fever duration, and IVIG resistance. The risk of bias was assessed using the Newcastle-Ottawa scale for cohort studies and Cochrane's Risk of Bias Tool for RCTs. The data were analyzed using the Review Manager software. Twelve studies with a total of 68,495 participants met the inclusion criteria. The incidences of CAAs (odds ratio [OR] = 0.93; 95% confidence interval [CI] = 0.64-1.34) and IVIG resistance (OR = 1.46; 95% CI = 1.00-2.12) did not differ significantly between no or low-dose versus high-dose aspirin in treating acute KD. Moreover, the fever durations (mean difference [MD] = 3.55 h; 95% CI = -7.99-15.10) and hospital stay lengths (MD = -0.54 days; 95% CI = -2.50-1.41) were similar in the no and low-dose aspirin group compared to the high-dose aspirin group. Our review indicates that there are no significant differences in the incidences of CAA and IVIG resistance, fever durations, and hospital stay lengths between no or low-dose versus high-dose aspirin in treating the acute phase of KD.
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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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.043 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".