Incidence of adverse events related to intravenous immunoglobulin therapy in children
Bibliographic record
Abstract
BACKGROUND: Intravenous immunoglobulin (IVIG) therapy is used in the treatment of pediatric diseases, although data about IVIG-related adverse events (IVIG-AEs) are limited. Objectives of this study were to document the incidence of IVIG-AEs in pediatric hospitalized patients and to identify risk factors for IVIG-AEs. METHODS: This retrospective cohort study included patients <18 years old who received IVIG therapy while admitted at a Canadian pediatric tertiary care center between 2016 and 2020. Patients and IVIG-perfusions characteristics were collected, as well as IVIG-AEs. Bivariate and multivariable logistic regressions were used to explore predictors of IVIG-AEs. RESULTS: We included 228 children, totaling 478 IVIG perfusions. Indications included treatment for inflammatory (52.6%), autoimmune disorders (35.5%), and immunoglobulin replacement (11.8%). A total of 213 IVIG-AEs were reported. Fever (13.6%) and headache (6.7%) were the most frequent IVIG-AEs. Most IVIG-AEs were mild (57%) or moderate (31%) in severity, but 12% were severe reactions. The following factors were predictive of IVIG-AEs in univariate analyses: older age (OR 1.14 [95% CI: 1.07-1.21]), dehydration (OR 2.55 [95% CI: 1.43-4.55]), concurrent allergies (OR 2.87 [95% CI: 1.26-6.56]), first perfusion (OR 1.53 [95% CI: 1.02-2.30]), and higher dosage (OR 2.14 [95% CI: 1.39-3.33]). Concurrent steroids decreased the risk of IVIG-AEs (OR 0.43 [95% CI: 0.19-0.96]). Older age and higher IVIG dose remained independent predictors of IVIG-AEs in multivariable analyses. CONCLUSIONS: Mild IVIG-AEs are frequent in children, and serious reactions may occur. Prospective studies are needed to confirm risk factors for IVIG-AEs and to evaluate how to best prevent them.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".