Escitalopram and duloxetine in the treatment of major depressive disorder: a pooled analysis of two trials
Bibliographic record
Abstract
Pooled analyses have shown that escitalopram has superior effectiveness versus all comparators, including selective serotonin reuptake inhibitors and venlafaxine. Recent studies have compared escitalopram with duloxetine. Data from two randomized, double-blind studies that compared escitalopram (10-20 mg/day) and duloxetine (60 mg/day) were pooled and analysed for all patients and for the subsample of severely depressed patients [baseline Montgomery-Asberg Depression Rating Scale (MADRS) score > or =30]. Escitalopram (n=280) was superior to duloxetine (n=284) with respect to mean change from baseline in MADRS score at weeks 1, 2, 4 and 8 with a mean treatment difference at week 8 of 2.6 points (P<0.01). Similar results were seen for severely depressed patients, with a mean treatment difference of 3.7 points (P<0.01). Response and remission rates at week 8 were significantly higher for patients treated with escitalopram [response 67.1% for escitalopram compared with 53.2% for duloxetine, P<0.001; remission (MADRS< or =12) 54.3% for escitalopram compared with 44.4% for duloxetine, P<0.05]. The numbers needed to treat based on response and remission rates, in favour of escitalopram, were 8 and 11, respectively, for all patients (6 and 7, respectively, for severely depressed patients). Significantly fewer (P<0.001) patients (all cause and owing to adverse events) withdrew from the escitalopram group. This pooled analysis shows that over an 8-week treatment period, escitalopram (10-20 mg/day) is superior in both effectiveness and tolerability compared with duloxetine (60 mg/day).
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.018 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".