Neuropsychiatric adverse drug reactions in children initiated on montelukast in real-life practice
Bibliographic record
Abstract
Although montelukast is generally well tolerated, postmarketing studies have reported serious neuropsychiatric adverse drug reactions (ADRs) leading to a United States Food and Drug Administration warning. The objective of this study was to determine the incidence of neuropsychiatric ADRs leading to discontinuation of montelukast in asthmatic children. We conducted a retrospective cohort study in children aged 1–17 years initiated on montelukast. In a nested cohort study, children initiated on montelukast as monotherapy or adjunct therapy to inhaled corticosteroids (ICS) were matched to those initiated on ICS monotherapy. A non-leading parental interview served to ascertain the occurrence of any ADRs with any asthma medication, and circumstances related to, and evolution of, the event. Out of the 106 participants who initiated montelukast, most were male (58%), Caucasian (62%) with a median (interquartile range) age of 5 (3–8) years. The incidence (95% CI) of drug cessation due to neuropsychiatric ADRs was 16 (10–26)%, mostly occurring within 2 weeks. Most frequent ADRs were irritability, aggressiveness and sleep disturbances. The relative risk of neuropsychiatric ADRs associated with montelukast versus ICS was 12 (2–90). In the real-life setting, asthmatic children initiated on montelukast experienced a notable risk of neuropsychiatric ADRs leading to drug cessation, that is significantly higher than that associated with ICS.
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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 | 0.003 |
| 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 | 0.001 | 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".