Clozapine Once‐Versus Multiple‐Daily Dosing Regimen: A Single‐Center, Retrospective, Cross‐Sectional Study
Bibliographic record
Abstract
BACKGROUND: Due to its short plasma half-life, clozapine is typically prescribed in a divided dosing regimen. Although once-daily dosing has proven effective in countries such as Japan, South Korea, Canada, and the United States, its adoption in real-world clinical practice in China remains unclear. This study aimed to compare patient characteristics, psychiatric symptoms, side effects, and plasma clozapine concentrations between once-daily and divided dosing regimens, and to determine the dosage required to achieve plasma levels of 350-600 ng/mL in the Chinese population. METHODS: We conducted a single-center, retrospective, cross-sectional study at Xi'an Mental Health Center, China, collecting data on clozapine-treated patients from March 2019 to March 2021. RESULTS: Most patients (80% of 198) in the Chinese cohort followed a divided-dose regimen in clinical practice. In the once-daily dosing group, the average plasma clozapine concentration was 158.75 ng/mL, with only 8% of patients reaching the therapeutic window. Conversely, patients on a divided dosing regimen had significantly higher plasma concentrations, averaging 373.34 ng/mL, with 28% within the therapeutic window. The psychiatric symptom remission rate did not differ significantly between the once-daily and divided-dosing groups (50.05% vs. 52.72%, p = 0.718); however, the divided-dosing group experienced a greater variety of adverse effects. Receiver operating characteristic analysis indicated that patients on multiple daily doses require a total daily clozapine dose of 150-250 mg to achieve the target plasma concentration, corresponding to a clozapine concentration-to-dose ratio of 1.4-2.4. CONCLUSIONS: Chinese clinicians generally adhere to a divided dosing schedule for clozapine when the daily dose exceeds 50 mg. Further prospective longitudinal studies are warranted to evaluate whether once-daily dosing regimens can improve clinical outcomes.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".