Atypical Antipsychotics in the Treatment of Depressive and Psychotic Symptoms in Patients with Chronic Schizophrenia: A Naturalistic Study
Bibliographic record
Abstract
Objectives. The aim of this naturalistic study was to investigate whether treatment with clozapine and other atypical antipsychotics for at least 2 years was associated with a reduction in psychotic and depressive symptoms and an improvement in chronic schizophrenia patients' awareness of their illness. Methods. Twenty-three adult outpatients (15 men and 8 women) treated with clozapine and 23 patients (16 men and 7 women) treated with other atypical antipsychotics were included in the study. Psychotic symptoms were evaluated using the Positive and Negative Syndrome Scale (PANSS), depressive symptoms were assessed with the Calgary Depression Scale for Schizophrenia (CDSS), and insight was assessed with the Scale to Assess Unawareness of Mental Disorder (SUMD). Results. The sample as a whole had a significant reduction in positive, negative, and general symptoms, whereas the reduction in depression was significant only for patients with CDSS scores of 5 and higher at the baseline. At the follow-up, patients treated with other atypical antipsychotics reported a greater reduction in depression than patients treated with clozapine, but not when limiting the analyses to those with clinically relevant depression. Conclusions. Atypical antipsychotics may be effective in reducing psychotic and depressive symptoms and in improving insight in patients with chronic schizophrenia, with no differences in the profiles of efficacy between compounds.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".