A review of acute treatments for bipolar depression
Bibliographic record
Abstract
Bipolar patients generally spend much more time in the depressed phase of their illness than the manic phase, and there are many more bipolar type II and bipolar spectrum disorder patients than there are bipolar type I. Additionally, there is a significant risk of suicide in bipolar patients when depressed. The treatment of the depressed phase of bipolar disorder is therefore a matter of some priority. Here, we review current evidence supporting the use of five groups of treatments: anti-depressants; lithium; anti-convulsants (valproate, and carbamazepine, lamotrigine, gabapentin); anti-psychotics; and other treatments (electroconvulsive therapy, benzodiazepines, sleep-deprivation, and dopamine agonists). From this review, it is apparent that the literature regarding the treatment of bipolar depression is significantly limited in several key areas. Nonetheless, from the evidence currently available, the treatments with the best evidence for efficacy are selective serotonin reuptake inhibitors (SSRIs) and lamotrigine. There is also some evidence in favour of bupropion and moclobemide. Although lithium and olanzapine monotherapies can also be beneficial, they appear less efficacious than antidepressants. One of the major concerns about treatment with antidepressants has been the risk of precipitating a switch into mania. However, recent studies suggest that, if a mood stabilizer and antidepressant are given concurrently, then the risk of switching is minimized. There is also recent evidence for an independent antidepressant action for at least one atypical antipsychotic. Therefore, the conclusion from this review, in contrast to previous suggestions, is that a combination of an atypical antipsychotic and either an SSRI or lamotrigine may provide a useful first-line treatment for depressed bipolar disorder patients. Further research is clearly required to examine this approach and compare it with other possible treatment options.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".