Treatment of mixed features in bipolar disorder
Bibliographic record
Abstract
Mood episodes with Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5)-defined mixed features are highly prevalent in bipolar disorder (BD), affecting ~40% of patients during the course of illness. Mixed states are associated with poorer clinical outcomes, greater treatment resistance, higher rates of comorbidity, more frequent mood episodes, and increased rates of suicide. The objectives of the current review are to identify, summarize, and synthesize studies assessing the efficacy of treatments specifically for BD I and II mood episodes (ie, including manic, hypomanic, and major depressive episodes) with DSM-5-defined mixed features. Two randomized controlled trials (RCTs) and 6 post-hoc analyses were identified, all of which assessed the efficacy of second-generation antipsychotics (SGAs) for the acute treatment of BD mood episodes with mixed features. Results from these studies provide preliminary support for SGAs as efficacious treatments for both mania with mixed features and bipolar depression with mixed features. However, there are inadequate data to definitively support or refute the clinical use of specific agents. Conventional mood stabilizing agents (eg, lithium and divalproex) have yet to have been adequately studied in DSM-5-defined mixed features. Further study is required to assess the efficacy, safety, and tolerability of treatments specifically for BD mood episodes with mixed features.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".