Canadian Network for Mood and Anxiety Treatments (CANMAT) and International Society for Bipolar Disorders (ISBD) recommendations for the management of patients with bipolar disorder with mixed presentations
Bibliographic record
Abstract
OBJECTIVES: The 2018 Canadian Network for Mood and Anxiety Treatments (CANMAT) and International Society for Bipolar Disorders (ISBD) guidelines provided clinicians with pragmatic treatment recommendations for bipolar disorder (BD). While these guidelines included commentary on how mixed features may direct treatment selection, specific recommendations were not provided-a critical gap which the current update aims to address. METHOD: Overview of research regarding mixed presentations in BD, with treatment recommendations developed using a modified CANMAT/ISBD rating methodology. Limitations are discussed, including the dearth of high-quality data and reliance on expert opinion. RESULTS: No agents met threshold for first-line treatment of DSM-5 manic or depressive episodes with mixed features. For mania + mixed features second-line treatment options include asenapine, cariprazine, divalproex, and aripiprazole. In depression + mixed features, cariprazine and lurasidone are recommended as second-line options. For DSM-IV defined mixed episodes, with a longer history of research, asenapine and aripiprazole are first-line, and olanzapine (monotherapy or combination), carbamazepine, and divalproex are second-line. Research on maintenance treatments following a DSM-5 mixed presentation is extremely limited, with third-line recommendations based on expert opinion. For maintenance treatment following a DSM-IV mixed episode, quetiapine (monotherapy or combination) is first-line, and lithium and olanzapine identified as second-line options. CONCLUSION: The CANMAT and ISBD groups hope these guidelines provide valuable support for clinicians providing care to patients experiencing mixed presentations, as well as further influence investment in research to improve diagnosis and treatment of this common and complex clinical state.
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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 | 0.013 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".