Escitalopram versus serotonin noradrenaline reuptake inhibitors as second step treatment for patients with major depressive disorder: a pooled analysis
Bibliographic record
Abstract
The objective of this study was to evaluate the efficacy and tolerability of escitalopram versus serotonin and noradrenaline reuptake inhibitors (SNRIs) as second step treatment (defined operationally as poor response or intolerability to an antidepressant) for major depressive disorder (MDD). Results from all eligible head-to-head clinical trials of MDD (which excluded patients who earlier failed two or more antidepressants) sponsored by Lundbeck or Forest comparing escitalopram and SNRIs (venlafaxine and duloxetine) were pooled. A second step treatment subgroup was identified, defined as patients treated earlier with any antidepressant in the 6-month period before baseline. Data from three clinical trials were included in the analysis; 132 patients were identified in the second step treatment subgroup (66 in each of the escitalopram and SNRI groups). The primary efficacy analysis showed that the patients subsequently treated with escitalopram had significantly lower Montgomery Asberg Depression Rating Scale total scores after 8 weeks compared with those subsequently treated with SNRIs (difference = -6.4, P<0.0001). Escitalopram treatment was also associated with higher clinical response (73 vs. 44%, P=0.0004) and remission rates (62 vs. 41%, P=0.0083) compared with subsequent treatment with SNRIs. Escitalopram showed a better tolerability profile with lower all-cause withdrawals from study (9 vs. 23%, P<0.04) and lower withdrawals because of adverse events (2 vs. 17%, P<0.003). In conclusion, escitalopram is associated with a better efficacy and tolerability profile than SNRIs (duloxetine and venlafaxine) when used as a second step treatment in patients with MDD. These results should be confirmed in prospective randomized clinical trials.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 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".