Treatment of immunoglobulin-resistant kawasaki disease: a Bayesian network meta-analysis of different regimens
Bibliographic record
Abstract
Background: This study aimed to gather evidence from clinical trials on the efficacy and safety of the available treatments for intravenous immunoglobulin (IVIG)-resistant Kawasaki disease (KD) in children. Methods: This work adopted the Newcastle-Ottawa scale to analyse the quality of the enrolled articles. A network meta-analysis was performed using clinical trials that compared drugs used to treat IVIG-resistant KD. Aggregate Data Drug Information System software v.1.16.5 was employed to analyse whether infliximab, second IVIG infusions, and intravenous pulse methylprednisolone (IVMP) were safe and effective. Results: Ten studies, involving 704 patients with IVIG-resistant KD, were identified and analysed. Overall, infliximab exhibited remarkable antipyretic activity compared with the second IVIG infusions (2.46, 1.00-6.94). According to the drug rank, infliximab was the best option against IVIG-resistant KD. Regarding adverse effects, the infliximab group was more prone to hepatomegaly. A second IVIG infusion was more likely to result in haemolytic anaemia. IVMP treatment was more susceptible to bradycardia, hyperglycaemia, hypertension, and hypothermia. In addition, infliximab, IVMP, and the second IVIG infusions showed no significant differences in the risk of developing a coronary artery aneurysm (CAA). Conclusion: Infliximab was the best option against IVIG-resistant KD, and IVMP, infliximab, and second IVIG infusions have not significant differences of prevent CAA in patients with IVIG-resistant KD. Systematic Review Registration: Identifier: https://osf.io/3894y.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".