Sertraline as an add-on treatment for depression symptoms in stable schizophrenia: A double-blind randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: There have been few studies to specifically examine the efficacy of selective serotonin reuptake inhibitors (SSRIs) for the symptoms of depression in schizophrenia. This study aimed to determine the efficacy of sertraline as a treatment for depressive symptoms in patients with stable schizophrenia. METHODS: A 12-week randomized, double-blind, placebo-controlled clinical trial was designed in 2010 with an active medication (sertraline) and a matching placebo. Sertraline was administrated 50-200 mg/daily. A total number of 60 patients were randomized into two groups in a 1:1 fashion. Calgary Depression Scale for Schizophrenia (CDSS) was used as the primary measure and Global Assessment of Functioning (GAF) scale was used as the sec- ondary measure. The data was analyzed by repeated measures analysis of variance (ANOVA) model to determine the effectiveness of sertraline. RSULTS: After 12 weeks, sertraline was significantly more effective than placebo in improving depressive symptoms in stable schizophrenia (p = 0.003). The mean score of GAF did not differ significantly in the sample population as a whole (p = 0.093). The difference between the two groups was not significant, either (p = 0.453). In addition, the rate of side effects was little but it was significantly more in the sertraline group (p < 0.001). CONCLUSIONS: The results of this study suggested sertraline to be useful as a treatment for depressive symptoms in patients with stable schizophrenia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".