Infliximab as a second‐line therapy for children with refractory Kawasaki disease: A systematic review and meta‐analysis of randomized controlled trials
Bibliographic record
Abstract
AIMS: Infliximab is a tumour necrosis factor-alpha inhibitor that is used to treat children with refractory Kawasaki disease (KD). Our purpose was to evaluate the safety and impact of infliximab versus intravenous immunoglobulins on the incidence of coronary artery aneurysms (CAAs) and treatment resistance in children with refractory KD. METHODS: The Medline/PubMed, Embase, CINAHL, Cochrane Central Register of Controlled Trials and clinical trials registries were searched to December 2021. Randomized controlled trials (RCTs) comparing infliximab as second-line therapy to a second dose of intravenous immunoglobulin (IVIG) in children with refractory KD, reported in abstract or full text, were included. Studies were selected and assessed for risk of bias by two reviewers. Data were extracted and pooled using conventional random-effects meta-analysis. The certainty of evidence was assessed using the GRADE system. RESULTS: A total of 199 participants from four RCTs were included. The pooled risk ratio (RR) for the incidence of treatment resistance in patients treated with infliximab was 0.40 (95% confidence interval [CI] 0.25-0.64). For incidence of CAAs RR was 1.20 (95% CI 0.54-2.63), the incidence of adverse effect "infusion reactions" RR was 0.48, (95% CI 0.12-1.92) and for "infections" RR was 0.55 (95% CI 0.27-1.12). Overall, the GRADE strength of evidence for the primary outcomes was low. Evidence on the duration of fever and inflammatory biomarkers was sparse, heterogeneous and inconclusive. CONCLUSION: Moderate-certainty evidence indicates that infliximab may reduce the incidence of treatment resistance in children with refractory KD. However, the limited strength of evidence warrants further research.
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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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.037 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".