Combining Antidepressants for Treatment-Resistant Depression
Bibliographic record
Abstract
OBJECTIVE: Many patients with depression remain poorly responsive to antidepressant monotherapy. One approach for managing treatment-resistant depression is to combine antidepressants and to capitalize on multiple therapeutic mechanisms of action. This review critically evaluates the evidence for efficacy of combining antidepressants. METHOD: A MEDLINE search of the last 15 years (up to June 2001), supplemented by a review of bibliographies, was conducted to identify relevant studies. Criteria used to select studies included (1) published studies with original data in peer-reviewed journals, (2) diagnosis of depression with partial or no response to standard treatments, (3) any combination of 2 antidepressants with both agents used to enhance antidepressant response, (4) outcome measurement of clinical response, and (5) sample size of 4 or more subjects. RESULTS: Twenty-seven studies (total N = 667) met the inclusion criteria, including 5 randomized controlled trials and 22 open-label trials. In the 24 studies (total N = 601) reporting response rates, the overall mean response rate was 62.2%. Methodological limitations included variability in definitions of treatment-resistant depression and response to treatment, dosing of medications, and reporting of adverse events. CONCLUSION: There is limited evidence, mostly in uncontrolled studies, supporting the efficacy of combination antidepressant treatment. Further randomized controlled trials with larger sample sizes are required to demonstrate the efficacy of a combination antidepressant strategy for patients with treatment-resistant depression.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".