Relative effectiveness of augmentation treatments for treatment-resistant depression: a systematic review and network meta-analysis
Bibliographic record
Abstract
Most interventions for treatment-resistant depression (TRD) are added as augmenters. We aimed to determine the relative effectiveness of augmentation treatments for TRD. This systematic review and network meta-analysis (NMA) sought all randomized trials of pharmacological and psychological augmentation interventions for adults meeting the most common clinical criteria for TRD. The NMA compared the intervention effectiveness of depressive symptoms for TRD augmentation. Of 36 included trials, 27 were suitable for inclusion in NMA, and no psychological trials could be included in the absence of a common comparator. Antipsychotics (13 trials), mood stabilizers (three trials), NMDA-targeting medications (five trials), and other mechanisms (3 trials) were compared against placebo. NMDA treatments were markedly superior to placebo (ES = 0.91, 95% CI 0.67 to 1.16) and head-to-head NMA suggested that NMDA therapies had the highest chance of being an effective treatment option compared to other pharmacological classes. This study provides the most comprehensive evidence of augmenters' effectiveness for TRD, and our GRADE recommendations can be used to guide guidelines to optimize treatment choices. Although conclusions are limited by paucity of, and heterogeneity between, trials as well as inconsistent reports of treatment safety. This work supports the use of NMDA-targeting medications such as ketamine.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.034 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".