Persistence in Therapy With Risperidone and Aripiprazole in Pediatric Outpatients
Bibliographic record
Abstract
OBJECTIVE: The practical effectiveness of second-generation antipsychotics in children and adolescents is an understudied issue. It is a crucial area of study, though, because such patients are often treated for long-lasting disorders. METHODS: We carried out a 24-month (March 2012-March 2014) observational study on an unselected population of pediatric outpatients treated with risperidone, aripiprazole, olanzapine, or quetiapine aiming to (1) describe drug use, (2) compare post hoc the discontinuation rates due to specific causes and dose adjustments by Kaplan-Meier analyses between drugs, and (3) analyze predictors influencing these outcomes by Cox multivariate models. RESULTS: Among 184 pediatric patients, 77% patients were prescribed risperidone, and 18% were prescribed aripiprazole. Olanzapine or quetiapine were scantly used; therefore, they were excluded from analyses. Risperidone was prevalent in younger, male patients with disruptive behavioral disorders; aripiprazole, in patients with tic disorders. Overall, discontinuations occurred mostly in the first 6 months, and, at 24 months, the discontinuation numbers were similar between users of risperidone and aripiprazole (41.5% vs 39.4%). In univariate analyses, dose reduction was higher for aripiprazole (P = .033). Multivariate analyses yielded the following predictors: for all-cause discontinuation, baseline severity (hazard ratio [HR] = 1.48, P = .001) and dose increase (HR = 3.55, P = .001); for patient-decided discontinuation, dose change (increase: HR = 6.43, P = .004; reduction: HR = 7.89, P = .049) and the presence of concomitant drugs (HR = 4.03, P = .034), while autistic patients discontinued less (HR = 0.23, P = .050); for clinician-decided discontinuation due to adverse drug reactions, baseline severity (HR = 1.96, P = .005) and dose increase (HR = 5.09, P = .016); for clinician-decided discontinuation due to inefficacy, baseline severity (HR = 2.88, P = .014) and the use of aripiprazole (HR = 5.55, P = .013); for dose increase, none; for dose reduction, the occurrence of adverse drug reactions (HR = 4.74, P = .046), while dose reduction was less probable in autistic patients (HR = 0.22, P = .042). CONCLUSIONS: The findings of this study show a similarity between the overall effectiveness of risperidone and aripiprazole in a real-life pediatric outpatient setting.
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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) | 0.001 | 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".