Kawasaki Disease and Anti-Inflammatory Doses of Aspirin—A Complicated Relationship
Bibliographic record
Abstract
Kawasaki disease (KD) is an acute febrile childhood illness and is the leading cause of acquired heart disease among children in high-income countries.The primary concern is the development of coronary artery aneurysms (CAAs), particularly large or giant CAAs that increase the risk of morbidity and mortality.The mainstay of acute KD management is intravenous immunoglobulin (IVIG) and aspirin (acetylsalicylic acid), and timely treatment with IVIG has been shown to decrease the risk of developing CAAs.Even before the introduction of IVIG as an effective therapy for KD in the 1980s, conventional therapy included aspirin for its anti-inflammatory effects at higher doses and its antiplatelet effects at lower doses.This practice continues today despite years of controversy and ample debate in the KD community about anti-inflammatory dosages of aspirin in terms of both effectiveness and appropriate dosing.North American centers traditionally have used high-dose aspirin (80-100 mg/kg per day) in the acute phase of KD, while centers in Japan have used medium-dose aspirin (30-50 mg/kg per day), citing concerns about potential adverse effects of higher doses.[1][2][3] After defervescence, low-dose aspirin (3-5 mg/kg per day) is typically continued for the antiplatelet effects for 6 to 8 weeks after disease onset and longer in the presence of CAAs.Despite this established use of anti-inflammatory doses of aspirin, there is mounting evidence that medium-dose or high-dose aspirin in the acute phase does not impact CAA outcomes.Recently,
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".