Combining Antidepressants in Acute Treatment of Depression: A Meta-Analysis of 38 Studies Including 4511 Patients
Bibliographic record
Abstract
OBJECTIVE: Combining antidepressants (ADs) for therapy of acute depression is frequently employed, but randomized studies have yielded conflicting results. We conducted a systematic review and meta-analysis aimed at determining efficacy and tolerability of combination therapy. METHODS: MEDLINE, Embase, PsycINFO, and CENTRAL databases were systematically searched through March 2014 for controlled studies comparing combinations of ADs with AD monotherapy in adult patients suffering from acute depression. The prespecified primary outcome was standardized mean difference (SMD), secondary outcomes were response, remission, and dropouts. RESULTS: Among 8688 articles screened, 38 studies were eligible, including 4511 patients. Combination treatment was statistically, significantly superior to monotherapy (SMD 0.29; 95% CI 0.16 to 0.42). During monotherapy, slightly fewer patients dropped out due to adverse events (OR 0.90; 95% CI 0.53 to 1.53). Studies were heterogeneous (I(2) = 63%), and there was indication of moderate publication bias (fail-safe N for an effect of 0.1:44), but results remained robust across prespecified secondary outcomes and subgroups, including analyses restricted to randomized controlled trials and low risk of bias studies. Meta-regression revealed an association of SMD with difference in imipramine-equivalent dose. Combining a reuptake inhibitor with an antagonist of presynaptic α2-autoreceptors was superior to other combinations. CONCLUSION: Combining ADs seems to be superior to monotherapy with only slightly more patients dropping out. Combining a reuptake inhibitor with an antagonist of presynaptic α2-autoreceptors seems to be significantly more effective than other combinations. Overall, our search revealed a dearth of well-designed studies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.000 | 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".