Impact of Second-Generation Antipsychotics and Perphenazine on Depressive Symptoms in a Randomized Trial of Treatment for Chronic Schizophrenia
Bibliographic record
Abstract
BACKGROUND: According to the American Psychiatric Association Clinical Practice Guidelines for schizophrenia, second-generation antipsychotics may be specifically indicated for the treatment of depression in schizophrenia. We examined the impact of these medications on symptoms of depression using the data from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE), conducted between January 2001 and December 2004. METHOD: Patients with DSM-IV-defined schizophrenia (N = 1,460) were assigned to treatment with a first-generation antipsychotic (perphenazine) or one of 4 second-generation drugs (olanzapine, quetiapine, risperidone, or ziprasidone) and followed for up to 18 months (phase 1). Patients with tardive dyskinesia were excluded from the randomization that included perphenazine. Depression was assessed with the Calgary Depression Scale for Schizophrenia (CDSS). Mixed models were used to evaluate group differences during treatment with the initially assigned drug. An interaction analysis evaluated differences in drug response by whether patients had a baseline score on the CDSS of ≥ 6, indicative of a current major depressive episode (MDE). RESULTS: There were no significant differences between treatment groups on phase 1 analysis, although there was a significant improvement in depression across all treatments. A significant interaction was found between treatment and experiencing an MDE at baseline (P = .05), and further paired comparisons suggested that quetiapine was superior to risperidone among patients who were in an MDE at baseline (P = .0056). CONCLUSIONS: We found no differences between any second-generation antipsychotic and the first-generation antipsychotic perphenazine and no support for the clinical practice recommendation, but we did detect a signal indicating a small potential difference favoring quetiapine over risperidone only in patients with an MDE at baseline.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".