Low Dose vs Standard Dose of Antipsychotics for Relapse Prevention in Schizophrenia: Meta-analysis
Bibliographic record
Abstract
BACKGROUND: It remains unknown as to whether the antipsychotic dose needed for the acute-phase treatment of schizophrenia is also necessary for relapse prevention. AIM: To compare the efficacy between standard dose [(World Health Organization daily defined dose (DDD)] vs low dose (≥50% to <1 DDD) or very low dose (<50% DDD) for relapse prevention in schizophrenia. DATA SOURCE: Double-blind, randomized, controlled trials with a follow-up duration of ≥24 weeks, including ≥2 dosage groups of the same antipsychotic drug for relapse prevention in schizophrenia, were searched using MEDLINE, the Cochrane Central Register of Controlled Trials, and EMBASE (last search: August 2009). DATA EXTRACTION: Data on overall treatment failure, hospitalization, relapse, and dropouts due to side effects were extracted and combined in a meta-analysis. DATA SYNTHESIS: Thirteen studies with 1395 subjects were included in this meta-analysis. Compared with the standard-dose treatment, the low-dose therapy did not show any statistically significant difference in overall treatment failure or hospitalization, while the standard dose showed a trend-level (P = .05) superiority in risk of relapse. The very low-dose group was inferior to the standard-dose group in all efficacy parameters. No significant difference was found in the rate of dropouts due to side effects between either standard dose vs low dose or very low dose. CONCLUSIONS: Although antipsychotic treatment with ≥50% to <1 DDD may be as effective as standard-dose therapy, there are insufficient clinical trial data to draw firm conclusions on standard- vs low-dose maintenance antipsychotic therapy for schizophrenia.
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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 | 0.021 | 0.039 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.070 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".