A retrospective study of antipsychotic drug switching in a pediatric population
Bibliographic record
Abstract
BACKGROUND: Antipsychotic drugs can be used to help treat a wide variety of psychiatric disorders. However, specific antipsychotic drugs for any particular patient may need to be changed for a number of different reasons, including a lack of therapeutic efficacy and / or intolerance to medication side-effects. Drug switching may occur through a limited number of established patterns. The nature of these changes is not well characterized in youth, despite their frequent occurrence. METHODS: A retrospective analysis of antipsychotic drug switches was conducted on patients who had been admitted as inpatients to a tertiary care child and adolescent psychiatric institute. PharmaNet (a large, central administrative database) records of all medications prescribed in the 52 weeks prior to admission, and then between admission and discharge, were analyzed for switching patterns. Additional data regarding diagnoses were obtained from medical chart review. RESULTS: Patients represented a diagnostically heterogeneous population, and almost all antipsychotic drugs were administered off-label. In the one year prior to and during admission to the hospital, a total of 31 out of 139 patients switched antipsychotic drugs. The frequency of switching increased closer to the time of admission, and the proportional rate of switching was even higher during hospital stay. The most common switch was from risperidone to quetiapine. Our analysis identified three main patterns of drug switching, all occurring with similar frequency: titrated drug switches, abrupt drug switches and concurrent drug administration. CONCLUSIONS: The present study indicates that antipsychotic drug switching in youth may be relatively common, particularly in the year prior to hospitalization. No specific manner of drug switching predominates. This study also demonstrates the feasibility of using large administrative databases to characterise switching patterns in youth.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".