Etanercept With IVIg for Acute Kawasaki Disease: A Randomized Controlled Trial
Bibliographic record
Abstract
OBJECTIVES: Patients with Kawasaki disease can develop life-altering coronary arterial abnormalities, particularly in those resistant to intravenous immunoglobulin (IVIg) therapy. We tested the tumor necrosis factor α receptor antagonist etanercept for reducing both IVIg resistance and coronary artery (CA) disease progression. METHODS: In a double-blind multicenter trial, patients with Kawasaki disease received either etanercept (0.8 mg/kg; n = 100) or placebo (n = 101) subcutaneously starting immediately after IVIg infusion. IVIg resistance was the primary outcome with prespecified subgroup analyses according to age, sex, and race. Secondary outcomes included echocardiographic CA measures within subgroups defined by coronary dilation (z score >2.5) at baseline. We used generalized estimating equations to analyze z score change and a prespecified algorithm for change in absolute diameters. RESULTS: IVIg resistance occurred in 22% (placebo) and 13% (etanercept) of patients (P = .10). Etanercept reduced IVIg resistance in patients >1 year of age (P = .03). In the entire population, 46 (23%) had a coronary z score >2.5 at baseline. Etanercept reduced coronary z score change in those with and without baseline dilation (P = .04 and P = .001); no improvement occurred in the analogous placebo groups. Etanercept (n = 22) reduced dilation progression compared with placebo (n = 24) by algorithm in those with baseline dilation (P = .03). No difference in the safety profile occurred between etanercept and placebo. CONCLUSIONS: Etanercept showed no significant benefit in IVIg resistance in the entire population. However, preplanned analyses showed benefit in patients >1 year. Importantly, etanercept appeared to ameliorate CA dilation, particularly in patients with baseline abnormalities.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".