Antipsychotics deprescribing in schizophrenia: trends and associated characteristics in Belgium and Québec
Bibliographic record
Abstract
Background : Antipsychotic polypharmacy (APP) is common worldwide in schizophrenia, while switch to antipsychotic monotherapy reduces adverse effects. Canada has been the leader in deprescribing policies in the last decades. Aims : To detect factors, including countries, associated with successful antipsychotic deprescribing after a psychiatric hospitalisation. Methods : Retrospective data were collected in a tertiary care hospital in Montreal (QC, Canada) and compared to data collected in 6 Belgian hospitals, in 2020-2021. Adult inpatients with a diagnosis of schizophrenia or schizoaffective disorder and discharged from a psychiatric unit after an acute hospitalisation were included. Results : At discharge, the daily number of antipsychotics had decreased in 22.2% of the 63 Canadian and 9.9% of the 516 Belgian patients, and increased in 17.5% of the Canadian and 24.3% of the Belgian patients. Living in a residential facility (OR=2.51, 95% CI 1.05-4.39), ≥2 previous antipsychotic trials (OR=15.38, 95% CI 3.62-65.36), having an antipsychotic side effect (OR=1.86, 95% CI 1.01-3.44), being in a general hospital (OR=2.28, 95% CI 1.09-4.75) and in Canada (OR=4.13, 95% CI 1.48-11.5) increased the odds of successful antipsychotic deprescribing at hospital discharge. Patients with a LAI (OR=0.51, 95% CI 0.26-0.98), prior clozapine use (OR=0.36, 95% CI 0.13-0.95), a greater antipsychotic exposure (OR=0.35, 95% CI 0.2-0.61) and a higher number of hypno-sedatives (OR=0.65, 95% CI 0.43-0.98) were less likely to have a deprescription. Conclusion : Antipsychotic deprescribing is feasible and already performed in identifiable patients, settings or situations. Patients hospitalised in Canada are more likely to have a deprescription than in Belgium.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".