A Retrospective Study on Infusion-Related Reactions to Rituximab in a Heterogeneous Pediatric Population
Bibliographic record
Abstract
OBJECTIVES: To assess risks and outcomes of infusion-related reactions to rituximab in a heterogeneous pediatric population. METHODS: All patients who received rituximab between July 2010 and July 2012 were retrieved from the pharmacy software and included for analysis. Data were collected according to 4 categories: demographic data, infusion data, infusion-related reactions, and biological data considered as risk factors (i.e., absolute lymphocyte count, lactate dehydrogenase levels). RESULTS: Sixty-seven patients treated for a total of 17 different indications were included. A total of 282 rituximab infusions were administered. Forty-three, mostly grade 1 or 2, infusion-related reactions occurred in 30 patients. Reactions occurred in 39.2% "first-dose" infusions, but this rate dropped drastically to 2.7% in subsequent doses. In multivariate analysis, high absolute lymphocyte count was the only risk factor for infusion-related reaction (OR = 1.03; 95% CI: 1.01-1.06; p = 0.014). CONCLUSIONS: Rituximab infusion-related reactions in a heterogeneous pediatric population were frequent on first infusion, but rare in subsequent ones. Overall, these reactions were mild and manageable through pharmacological treatment. Patients with an elevated absolute lymphocyte count before infusion were at greater risk for an infusion-related reaction.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".