Effects of Discontinuation of Drugs Used for Augmentation Therapy on Treatment Outcomes in Depression: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
INTRODUCTION: There has been no consensus on whether and how long add-on drugs for augmentation therapy should be continued in the treatment of depression. METHODS: Double-blind randomized controlled trials that examined the effects of discontinuation of drugs used for augmentation on treatment outcomes in patients with depression were identified. Meta-analyses were performed to compare rates of study withdrawal due to any reason, study-defined relapse, and adverse events between patients who continued augmentation therapy and those who discontinued it. RESULTS: Seven studies were included (n=841 for continuing augmentation therapy; n=831 for discontinuing augmentation therapy). The rate of study withdrawal due to any reason was not significantly different between the 2 groups (risk ratio [RR]=0.86, 95% confidence interval [CI]=0.69-1.08, p=0.20). Study withdrawal due to relapse was less frequent in the continuation group than in the discontinuation group (RR=0.61, 95% CI=0.40-0.92, p=0.02); however, this statistical significance disappeared when one study using esketamine as augmentation was excluded. Analysis of the data from 5 studies that included a stabilization period before randomization found less frequent relapse in the continuation group than in the discontinuation group (RR=0.47, 95% CI=0.36-0.60, p<0.01). This finding was repeated when the esketamine study was excluded. DISCUSSION: No firm conclusions could be drawn in light of the small number of studies included. Currently available evidence suggests that add-on drugs, other than esketamine, used for augmentation therapy for depression may be discontinued. This may not be the case for patients who are maintained with augmentation therapy after remission.
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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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".