Escitalopram in the treatment of major depressive disorder in primary-care settings: an open-label trial
Bibliographic record
Abstract
Background: The present trial was designed to assess the efficacy and safety of escitalopram prescribed to patients seeking treatment of major depressive disorder (MDD) in a Canadian primary-care setting. Methods: Investigators (mainly primary-care physicians) enrolled patients with MDD from their daily practice. Patients were treated with escitalopram (flexible dose 10–20 mg/day) for up to 24 weeks. Efficacy assessments included the Montgomery–Åsberg Depression Rating Scale (MADRS), the Clinical Global Impression-Improvement and -Severity scales (CGI-I, CGI-S), the Patient Global Evaluation (PGE), and the Medical Outcome Study 36-item Short Form (SF-36). Results: Out of the 647 patients enrolled, 461 (71%) completed 24 weeks of treatment. The most common reason for discontinuation was adverse events (10%). The mean MADRS score decreased from 30.7 at baseline to 10.9 at the end of 24 weeks (last observation carried forward, LOCF). Remission (MADRS≤12) was achieved by 65.5% of patients (LOCF). Symptom improvements were confirmed by global ratings of improvement made by physicians (CGI-I) as well as patients PGE. There was improvement on all dimensions of the SF-36, suggesting an overall improvement in quality of life. Conclusions: Escitalopram was well tolerated, safe, and efficacious. Escitalopram can be used with confidence to treat patients with MDD in Canadian primary-care settings. Depression and Anxiety, 2008. © 2008 Wiley-Liss, Inc.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".