Comparative Effect of Escitalopram and Citalopram on Depressive Symptoms of Patients with Schizophrenia: A Double-Blind Randomized Controlled Trial
Bibliographic record
Abstract
Objectives: Given the importance of the management of depression in patients with schizophrenia, this research was carried out to compare the therapeutic effects of escitalopram and citalopram on depressive symptoms of these patients. Material and Methods: This double-blind randomized controlled trial was conducted on 60 patients diagnosed with schizophrenia. The patients who had depression based on the Calgary Depression Scale for Schizophrenia (CDSS) were included in the study. The first group received 10–40 mg/day of citalopram, and the second group received 5–20 mg/day of escitalopram. The patients were treated for 8 weeks. Any reduction in depressive symptoms based on the CDSS measure was considered the primary outcome of the research. Results: Although the two groups had no significant difference in CDSS score at the initial assessment (P = 0.86), after 8 weeks, the depressive symptoms decreased and a significant difference was observed between the two groups (P = 0.03). The two groups had no significant difference in drug side effects, except for anorexia (P = 0.03). Conclusion: Depressive symptoms decreased significantly after 8 weeks of treatment with both citalopram and escitalopram; however, these symptoms had more reduction in patients taking escitalopram than the individuals receiving citalopram.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".