Clozapine once- versus multiple-daily dosing: a two-center cross-sectional study, systematic review and meta-analysis
Bibliographic record
Abstract
Evidence regarding effectiveness and safety of clozapine once- vs. multiple-daily dosing is limited. We compared demographic and clinical parameters between patients with once- vs. multiple-daily dosing in the Department of Psychiatry and Psychotherapy, University of Regensburg, Germany (AGATE dataset), and the Department of Psychiatry, Lausanne University Hospital, Switzerland, using non-parametric tests. Effectiveness and safety outcomes were available in the AGATE dataset. We performed a systematic review in PubMed/Embase until February 2022, meta-analyzing studies comparing clozapine once- vs. multiple-daily-dosing. We estimated a pooled odds ratio for adverse drug-induced reactions (ADRs) and meta-analyzed differences regarding clinical symptom severity, age, percentage males, smokers, clozapine dose, and co-medications between patients receiving once- vs. multiple-daily dosing. Study quality was assessed using the Newcastle-Ottawa-Scale. Of 1494 and 174 patients included in AGATE and Lausanne datasets, clozapine was prescribed multiple-daily in 74.8% and 67.8%, respectively. In the AGATE cohort, no differences were reported for the clinical symptoms severity or ADR rate (p > 0.05). Meta-analyzing eight cohorts with a total of 2810 clozapine-treated individuals, we found more severe clinical symptoms (p = 0.036), increased ADR risk (p = 0.01), higher clozapine doses (p < 0.001), more frequent co-medication with other antipsychotics (p < 0.001), benzodiazepines (p < 0.001), anticholinergics (p = 0.039), and laxatives (p < 0.001) in patients on multiple- vs. once-daily dosing. Of six studies, five were rated as good, and one as poor quality. Patients responding less well to clozapine may be prescribed higher doses multiple-daily, also treated with polypharmacy, potentially underlying worse safety outcomes. Patient preferences and adherence should be considered during regimen selection.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.037 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".