Treatment of mixed bipolar states
Bibliographic record
Abstract
Mixed bipolar states are associated with more severe symptoms and outcome. Our aim is to review the literature examining their treatment. We conducted a literature search of randomized clinical studies and post-hoc analyses on mixed bipolar states' treatment. Remarkably, there is only one double-blind, placebo-controlled trial, recruiting a mixed episode cohort, and one post-hoc analysis of this trial, while most data come from post-hoc analyses of trials including both manic and mixed patients. Improvement of manic symptoms in mixed episodes is similar to that seen in pure manic episodes and independent of baseline depressive features. The magnitude of response to manic symptoms' treatment probably exceeds that of depressive symptoms, which appear to resolve later. Valproate and carbamazepine are effective in acute mixed episodes, but the efficacy of lithium appears questionable. Atypical antipsychotic monotherapy improves both manic and depressive symptoms. Mood-stabilizer-atypical antipsychotic combination increases this effect. Atypical antipsychotic-antidepressant combination against acute mixed depression does not increase the risk for mania, although its superior efficacy vs. atypical antipsychotic monotherapy cannot be supported by current data. As regards prophylaxis, atypical antipsychotic monotherapy is associated with a lower incidence of and a longer time to relapse of any kind. The augmentation of lithium or divalproex with atypical antipsychotics increases prophylactic efficacy. Lithium or divalproex monotherapy have not been associated with significant prophylactic benefits following mixed mania. New, randomized prospective trials involving homogeneous cohorts of mixed bipolar patients are needed in order to delineate the appropriate pharmacological treatment of mixed states.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".