No Effects of Antidepressants on Negative Symptoms in Schizophrenia
Bibliographic record
Abstract
Negative symptoms are common in schizophrenia, but often difficult to differentiate from depression. They are associated with long-term impairment and do not respond well to current treatment approaches. Even though antidepressants are commonly prescribed in schizophrenia, their beneficial effect is still under debate. In the present study, we aimed to investigate the effect of serotonergic versus noradrenergic antidepressant add-on therapy on negative symptoms in schizophrenia. Fifty-eight patients with schizophrenia according to Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition criteria and with predominant negative symptoms were randomized in a double-blind design to add-on treatment with citalopram, reboxetine, or placebo for 4 weeks. Analysis of covariance with repeated-measures design was used to compare improvement between treatment groups in scores of the Positive and Negative Syndrome Scale and the Hamilton Rating Scale for Depression. A χ² test was used to compare responder rates between treatment groups. Repeated-measures analysis of covariance revealed no differences between treatment groups over time (treatment × time, not statistically significant) for Positive and Negative Syndrome Scale subscales. Although a subgroup analysis in subjects fulfilling the criteria for minor depression was suggestive of higher responder rates in the citalopram group compared with reboxetine, the results did not reach significance level. Our findings do not support a beneficial effect of adjunctive antidepressant treatment on negative symptoms in schizophrenia. However, depressive symptoms are reduced in patients with minor depression by citalopram but not reboxetine, which is in line with previous findings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".