Prescribing and deprescribing trends in schizophrenia: An overview of inpatients in Belgium and in the Canadian province of Québec
Bibliographic record
Abstract
Although switching to antipsychotic monotherapy improves patient outcomes in schizophrenia, antipsychotic deprescribing is rarely performed, and its use varies between countries, as do psychotropic prescribing patterns. This study aimed to determine factors associated with antipsychotic deprescribing at discharge after a psychiatric hospitalization and to compare psychotropic prescribing patterns between Belgium and Québec, Canada. Data on adult inpatients with schizophrenia were collected retrospectively in seven hospitals. At discharge, the number of antipsychotics had decreased in 22.2% of the 63 Canadian patients and 9.9% of the 516 Belgian patients. A number of factors increased the likelihood of antipsychotic deprescribing: a hospitalization in the Canadian hospital (aOR = 4.13, 95% CI 1.48-11.5), living in a residential facility (aOR = 2.51, 95% CI 1.05-4.39), ≥2 previous antipsychotic trials (aOR = 15.38, 95% CI 3.62-65.36), having an antipsychotic side effect (aOR = 1.86, 95% CI 1.01-3.44) and being in a general hospital (aOR = 2.28, 95% CI 1.09-4.75). Patients on a long-acting injectable antipsychotic (aOR = 0.51, 95% CI 0.26-0.98), with prior clozapine use (aOR = 0.36, 95% CI 0.13-0.95), greater antipsychotic exposure (aOR = 0.35, 95% CI 0.2-0.61) and more hypno-sedatives (aOR = 0.65, 95% CI 0.43-0.98), were less likely to be deprescribed. Specific deprescribing interventions could target patients who are less likely to be deprescribed.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".