Clozapine Dose Reduction in Remitted Patients With Treatment-Resistant Schizophrenia: A Case Series
Bibliographic record
Abstract
BACKGROUND: There is limited evidence on the outcomes of clozapine deprescribing in remitted treatment-resistant schizophrenia (TRS) patients. We present a series of TRS patients in remission who underwent progressive reductions in their maintenance clozapine dose. STUDY DESIGN: This was a retrospective chart review of patients treated with clozapine from March 20, 2014 to March 20, 2024, at the Centre for Addiction and Mental Health, Toronto, Canada. We included patients who met criteria for TRS, were in remission on clozapine, and underwent progressive reduction > 25% in their maintenance dose. STUDY RESULTS: Of the 9 cases included, 4 (44.4%) maintained remission following progressive dose reductions. Two patients (22.2%) relapsed and were hospitalized, while 3 (33.3%) relapsed and required an increase in clozapine dose. Of those who relapsed, 3 had subtherapeutic clozapine levels and one had stopped clozapine before relapse. Overall dose reductions ranged from 100% (complete discontinuation with sustained remission) to 25% (with subsequent dose increase after relapse). The average rate of clozapine reduction was slow across cases, with a median of 25 mg every 12 weeks. Although not statistically significant, patients who maintained remission had slower tapering rates, ranging from 25 mg every 5 weeks to 25 mg every 2 years. CONCLUSIONS: Reducing the clozapine maintenance dose in remitted TRS patients carried a substantial risk of relapse. The risk may be lower when dose reductions are guided by clozapine levels and implemented gradually over several months to years. Larger samples are needed to identify predictors of relapse in TRS patients undergoing clozapine deprescribing.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".