Efficacy and acceptability of mood stabilisers in the treatment of acute bipolar depression: systematic review
Bibliographic record
Abstract
BACKGROUND: Although people with bipolar disorder spend more time in a depressed than manic state, little evidence is available to guide the treatment of acute bipolar depression. AIMS: To compare the efficacy, acceptability and safety of mood stabiliser monotherapy with combination and antidepressant treatment in adults with acute bipolar depression. METHOD: Systematic review and meta-analysis of randomised, double-blind controlled trials. RESULTS: Eighteen studies with a total 4105 participants were analysed. Mood stabiliser monotherapy was associated with increased rates of response (relative risk (RR) = 1.30, 95% CI 1.16-1.44, number needed to treat (NNT) = 10, 95% CI 7-18) and remission (RR = 1.51, 95% CI 1.27-1.79, NNT = 8, 95% CI 5-14) relative to placebo. Combination therapy was not statistically superior to monotherapy. Weight gain, switching and suicide rates did not differ between groups. No differences were found between individual medications or drug classes for any outcome. CONCLUSIONS: Mood stabilisers are moderately efficacious for acute bipolar depression. Extant studies are few and limited by high rates of discontinuation and short duration. Further study of existing and novel agents is required.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".