Add‐on mirtazapine improves depressive symptoms in schizophrenia: a double‐blind randomized placebo‐controlled study with an open‐label extension phase
Bibliographic record
Abstract
Depression is common in schizophrenia and worsens its course. The role of antidepressants for schizophrenic depression remains unclear. In this study, the efficacy of add-on mirtazapine on depression in schizophrenia was explored in a subsidiary arm of a recent randomized controlled trial. Patients (n = 41) with chronic but stable schizophrenia and inadequate response to stable doses of different first-generation antipsychotics were treated with add-on mirtazapine 30 mg or placebo during a 6-week double-blind phase and with open-label add-on mirtazapine during a 6-week extension phase. Efficacy measures were the Calgary Depression Scale for Schizophrenia (CDSS) and the Positive and Negative Syndrome Scale depression item. During the double-blind phase, both measures' scores decreased significantly in the mirtazapine group but not in the placebo group (for the CDSS, 52.0% vs 19.6%, respectively). During the open‐label phase, both groups demonstrated significant improvements. In between‐group comparison, a trend favoring mirtazapine did not reach statistical significance. The changes in the CDSS correlated positively with those in the Positive and Negative Syndrome Scale negative, positive and total (sub)scales for mirtazapine‐treated patients during the double‐blind phase. Depressed patients with schizophrenia may benefit from mirtazapine–first‐generation antipsychotics combination, with no increased risk for psychosis. However, more studies are needed.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".