A Metaanalysis of Clinical Trials Comparing Moclobemide with Selective Serotonin Reuptake Inhibitors for the Treatment of Major Depressive Disorder
Bibliographic record
Abstract
OBJECTIVE: To compare response rates among patients with major depressive disorder (MDD) treated with either moclobemide, an antidepressant thought to simultaneously enhance both noradrenergic and serotonergic neurotransmission, or selective serotonin reuptake inhibitors (SSRIs). METHODS: Using a random-effects model, we combined 12 trials involving 1207 outpatients with MDD. RESULTS: Patients treated with moclobemide were as likely to experience clinical response as those treated with SSRIs (risk ratio 1.08; 95% confidence interval, 0.92 to 1.26; P = 0.314). Simply pooling response rates for the 2 agents resulted in a 62.1% response rate for moclobemide and a 57.5% response rate for the SSRIs. A metaregression did not reveal a statistically significant relation between the mean moclobemide dosage for each study and the risk ratio for response rates. Further, we found no difference between the 2 treatments in overall discontinuation rates, discontinuation rates due to adverse events, or discontinuation rates due to lack of efficacy. Also, rates of fatigue or somnolence and of insomnia were similar between the 2 treatment groups. However, SSRI treatment was associated with higher rates of nausea, headaches, and treatment-emergent anxiety than was treatment with moclobemide. CONCLUSIONS: These results suggest that moclobemide and the SSRIs do differ with respect to their side effect profiles but not in their overall efficacy in the treatment of MDD.
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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 | 0.019 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.039 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".