Dopaminergic agents in the treatment of bipolar depression: a systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: To systematically examine the effects of dopaminergic agents (modafinil, armodafinil, pramipexole, methylphenidate, and amphetamines) on bipolar depression outcomes. METHODS: Meta-analysis of randomized controlled trials was performed to assess the efficacy and safety of treatment with dopaminergic agents in bipolar depression. In a secondary analysis, findings from both randomized controlled trials and high-quality observational studies were pooled by means of meta-analytic procedures to explore dopaminergic treatment-related new mania. RESULTS: Nine studies (1716 patients) were included in our meta-analysis of randomized controlled trials. Treatment with dopaminergic agents for bipolar depression was associated with an increase in both response (1671 individuals, RR 1.25, 95% CI 1.05 to 1.50) and remission rates (1671 individuals, RR 1.40, 95% CI 1.14, 1.71). There was no evidence of an increased risk of mood switch associated with this treatment (1646 individuals, RR 0.96, 95% CI 0.49, 1.89). Our secondary analysis (1231 individuals) yielded a cumulative incidence of mood switch of 3% (95% CI 1.0, 5.0) during a mean follow-up period of 7.5 months. CONCLUSIONS: Preliminary findings suggest that dopaminergic agents may represent a useful alternative for the treatment of bipolar depression, with no evidence for a related increase in the risk of mood destabilization during short-term follow-up.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.027 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".