Open prospective study of ziprasidone in patients with schizophrenia with depressive symptoms: A multicenter study
Bibliographic record
Abstract
AIMS: The goal of this study was to examine the efficacy and safety of ziprasidone to treat depressive symptoms in Korean patients with schizophrenia who showed stable symptoms. METHODS: In this 8-week, open-label, prospective, non-randomized, multicenter study, 34 patients with schizophrenia who showed a stable response to previous medications, maintained a stable dose, and who had depressive symptoms, were recruited. Ziprasidone was the only antipsychotic agent allowed for 8 weeks after a 2-7-week washout period. RESULTS: Steady decreases were observed on the Montgomery-Asberg Depression Rating Scale, the Calgary Depression Scale for Schizophrenia, the Positive and Negative Syndrome Scale, and the Clinical Global Impression-Severity Scale scores. The Montgomery-Asberg Depression Rating Scale score was 20.26 ± 4.77 at baseline and 12.21 ± 7.94 at the end-point (P < 0.01). The Calgary Depression Scale for Schizophrenia score was 9.76 ± 4.11 at baseline and 5.00 ± 3.94 at the end-point (P < 0.01). The Positive and Negative Syndrome Scale total score was 75.24 ± 22.63 at baseline and 66.53 ± 24.28 at the end-point (P < 0.01). The Clinical Global Impression-Severity Scale score was 3.44 ± 0.66 at baseline and 3.15 ± 0.86 at the end-point (P < 0.05). No significant differences were observed for total scores on the Simpson and Angus Rating Scale, the Barnes Akathisia Rating Scale, or the Abnormal Involuntary Movement Scale between the baseline and end-point. CONCLUSIONS: Ziprasidone was effective for improving depressive symptom scores and was well tolerated. Switching to ziprasidone is a good strategy in patients with schizophrenia who are experiencing depressive symptoms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".