Ketamine for Bipolar Depression: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Ketamine appears to have a therapeutic role in certain mental disorders, most notably unipolar major depressive disorder. However, its efficacy in bipolar depression is less clear. This study aimed to assess the efficacy and tolerability of ketamine for bipolar depression. METHODS: We conducted a systematic review of experimental studies using ketamine for the treatment of bipolar depression. We searched PubMed, MEDLINE, Embase, PsycINFO, and the Cochrane Central Register for relevant studies published since each database's inception. We synthesized evidence regarding efficacy (improvement in depression rating scores) and tolerability (adverse events, dissociation, dropouts) across studies. RESULTS: We identified 6 studies, with 135 participants (53% female; 44.7 years; standard deviation, 11.7 years). All studies used 0.5 mg/kg of add-on intravenous racemic ketamine, with the number of doses ranging from 1 to 6; all participants continued a mood-stabilizing agent. The overall proportion achieving a response (defined as those having a reduction in their baseline depression severity of at least 50%) was 61% for those receiving ketamine and 5% for those receiving a placebo. The overall response rates varied from 52% to 80% across studies. Ketamine was reasonably well tolerated; however, 2 participants (1 receiving ketamine and 1 receiving placebo) developed manic symptoms. Some participants developed significant dissociative symptoms at the 40-minute mark following ketamine infusion in 2 trials. CONCLUSIONS: There is some preliminary evidence supporting use of intravenous racemic ketamine to treat adults with bipolar depression. There is a need for additional studies exploring longer-term outcomes and alterative formulations of 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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".