Antidepressive Effectiveness of Amisulpride, Aripiprazole, and Olanzapine in Patients With Schizophrenia Spectrum Disorders
Bibliographic record
Abstract
BACKGROUND: Depressive symptoms are frequent in schizophrenia and associated with a poorer outcome. Currently, the optimal treatment for depressive symptoms in schizophrenia remains undetermined. Amisulpride, aripiprazole, and olanzapine all have antidepressive pharmacodynamic properties, ranging from serotonergic affinities to limbic dopaminergic selectivity. Consequently, in a 12-month pragmatic, randomized clinical trial, we aimed to investigate differences in antidepressive effectiveness among amisulpride, aripiprazole, and olanzapine as a secondary outcome, measured by change in the Calgary Depression Scale for Schizophrenia sum score in patients within the schizophrenia spectrum. METHODS: Psychotic patients within the schizophrenia spectrum were included, and effectiveness was analyzed with latent growth curve modeling. RESULTS: Of the 144 patients, 51 (35%) were women, the mean age was 31.7 (SD 12.7), and 39% were antipsychotic naive. At inclusion, 68 (47%) participants had a Calgary Depression Scale for Schizophrenia sum score >6, indicating severe depressive symptoms. Across the 12-month follow-up, there was a depressive symptom reduction in all medication groups, but no statistically significant differences between the study drugs. Separate analyses of the subcohort with elevated depressive symptoms at inclusion also failed to find differences in depressive symptom reduction between study drugs. The reduction in depressive symptoms mainly occurred within 6 weeks after randomization. CONCLUSIONS: There was a reduction in depressive symptoms under treatment with amisulpride, aripiprazole, and olanzapine in acutely psychotic patients with schizophrenia spectrum disorder, but no differences between the drugs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".