Bibliographic record
Abstract
OBJECTIVE: To review the efficacy of pharmacological agents in bipolar mixed states. METHODS: We conducted a PubMed search of all English-language articles involving Food and Drug Administration (FDA)-approved agents for manic/mixed states in adults with bipolar I disorder. We also included names of agents established as efficacious in acute mania/mixed states that have not received FDA approval for bipolar disorder. Bibliographies from relevant articles were also searched. The efficacy of each agent in the mixed state subpopulation was reviewed, as evidenced by change from baseline on total scores of mania [e.g., Young Mania Rating Scale (YMRS)] and depression [e.g., Montgomery-Åsberg Depression Rating Scale (MADRS)] measures. RESULTS: No available study is dedicated exclusively to the evaluation of mixed state populations. Although key inclusion and exclusion criteria are similar across treatment studies, mixed states have been variably defined and measured. The use of conventional manic and depressive metrics in bipolar mixed states perpetuates the unproven notion that mixed states are the consequence of coexisting depression and mania. Notwithstanding the methodological limitations, there are numerically more studies that exist for atypical antipsychotic agents than for any other class. On the basis of symptomatic improvement, recommendations for and/or strong admonishments against any established antimanic agents (e.g., lithium) cannot be made. An emergent signal supports combination treatment strategies (e.g., atypical antipsychotic plus divalproex) over mood stabilizer monotherapy (e.g., divalproex). Available evidence does not empirically support the hypothesis that conventional antipsychotics engender and/or amplify depressive symptoms in bipolar mixed states. CONCLUSIONS: All proven antimanic agents (including lithium), can be recommended in the treatment of mixed/dysphoric states. The totality of evidence with attention paid to the therapeutic index of each agent would suggest that atypical antipsychotics and divalproex be considered as first-line treatment, with lithium and carbamazepine as second-line. Most individuals will require combination therapy for the treatment of mixed states; variable combinations of atypical antipsychotics and conventional mood stabilizers have the most replicated evidence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 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) | 0.004 | 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".