Increasing Versus Maintaining the Dose of Olanzapine or Risperidone in Schizophrenia Patients Who Did Not Respond to a Modest Dosage
Bibliographic record
Abstract
OBJECTIVE: While doctors often increase the dose of an antipsychotic when there is insufficient response, there is limited evidence that this intervention is any better than waiting longer on the lower dose. We put the proposition to test. METHOD: In this 4-week, double-blind, randomized controlled trial conducted in psychiatric care from September 2012 to March 2015, 103 patients with schizophrenia (ICD-10) who did not respond to olanzapine 10 mg/d or risperidone 3 mg/d were randomly allocated to a dose-increment or -continuation group. In the increment group, antipsychotic doses were doubled for 4 weeks, whereas in the continuation group, doses were not changed. Completion rate (primary outcome measure); changes in psychopathology, function, and extrapyramidal symptoms; and response rate were compared between the groups. The relationship between baseline plasma antipsychotic concentrations and changes in psychopathology was examined. RESULTS: The completion rate was significantly lower in the increment group than in the continuation group (69.2% [36/52] vs 86.3% [44/51], P = .038). No significant superiority was observed in any of the outcome measures in the increment group compared to the continuation group, except the Positive and Negative Syndrome Scale (PANSS) positive subscale score change in intention-to-treat analysis. Those with lower plasma concentrations of olanzapine on their initial treatment showed a greater improvement on the PANSS positive subscale when their dose was increased (P = .042). CONCLUSIONS: As a general strategy, patients with schizophrenia failing to respond to moderate antipsychotic doses may not benefit from an increase in dose. The possibility of benefit in those whose plasma antipsychotic concentrations at baseline are still low cannot be ruled out. TRIAL REGISTRATION: UMIN.ac.jp/ctr/index.htm identifier: UMIN000008667.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".