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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".