Optimal Doses of Specific Antipsychotics for Relapse Prevention in a Nationwide Cohort of Patients with Schizophrenia
Bibliographic record
Abstract
BACKGROUND AND HYPOTHESIS: Optimal doses of most antipsychotics in the maintenance treatment of schizophrenia are unknown. We aimed to study the risk of severe relapse indicated by rehospitalization for different dose categories of 15 most frequently used antipsychotics in monotherapy in Finland. STUDY METHODS: We studied the risk of rehospitalization (Adjusted Hazard Ratio, aHR) associated with six antipsychotic monotherapy dose categories (as time-varying dose, measured in defined daily dose, DDDs/day) in a nationwide cohort of persons diagnosed with schizophrenia (n = 61 889), using within-individual analyses to eliminate selection bias. STUDY RESULTS: Among the 15 most widely used antipsychotics, 13 had a U- or J-shaped dose-response curve, showing the lowest risks of relapse for doses of 0.6-<1.1 DDDs/day vs nonuse of antipsychotics. The exceptions were oral perphenazine (aHR = 0.72, 95% CI = 0.68-0.76, <0.6 DDDs/day), and olanzapine-long-acting injectable (LAI), which had the lowest aHR of any antipsychotic (aHR = 0.17, 95% CI = 0.11-0.25, 1.4-<1.6 DDDs/day). Certain risperidone and perphenazine doses <0.9 DDD/day were associated with 21%-45% lower risk of rehospitalization (P < .001) than the standard dose of 0.9-1.1 DDD/day (ie, 5 mg for risperidone and 30 mg for perphenazine). CONCLUSIONS: For most antipsychotics, the risk of severe relapse was the lowest during use of standard dose. Our results suggest that olanzapine LAI is highly effective in dose ranges >0.9 DDD/day, and especially at 1.4-<1.6 DDDs/day (405 mg/4 weeks) associated with substantially lower risk of rehospitalization than any dose of any other antipsychotic. The current WHO standard dose definitions appear to be clearly too high for perphenazine and somewhat too high for risperidone.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".