Computerized cognitive behavioral therapy with sertraline in schizophrenia with depressive episodes: A 12-week randomized controlled trial
Bibliographic record
Abstract
BACKGROUND Schizophrenia is commonly associated with comorbid depression, which exacerbates cognitive impairments and negatively impacts quality of life. Despite the high prevalence and burden of these comorbidities, effective treatment options, particularly for cognitive dysfunction, remain limited. AIM To evaluate the efficacy of computerized cognitive behavioral therapy (CCBT) with sertraline vs sertraline monotherapy in improving depressive symptoms, cognitive function, and quality of life in schizophrenia and depressive episodes. METHODS In this single-center, randomized controlled trial, 68 adults [mean age (SD) = 36.5 (10.0), 57.4% male] with schizophrenia and depressive symptoms were randomly assigned to receive either CCBT with sertraline or sertraline monotherapy during a 4-week hospitalization. The CCBT intervention involved 45-60-minute sessions twice weekly for four weeks. Outcomes included comparisons of depressive symptoms (Calgary depression scale for schizophrenia), cognitive function [MATRICS consensus cognitive battery (MCCB)], and quality of life (36-item short form survey) between the groups. RESULTS The experimental group showed greater improvements in depressive symptoms at 4, 8, and 12 weeks compared to the controls, with the most notable difference at 12 weeks [mean difference (MD) = -1.7; P < 0.001; Cohen’s d = 0.9]. Cognitive function improved across all MCCB domains in the experimental group, with higher processing speed scores (MD = 4.1; P = 0.043; Cohen’s d = 0.5) and social cognition scores (MD = 4.9; P = 0.006; Cohen’s d = 0.7) than in the control group. Quality of life, particularly in mental health, was significantly better in the experimental group. CONCLUSION CCBT with sertraline was more effective than sertraline monotherapy for patients with schizophrenia and depressive episodes, supporting its use as an adjunctive therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".