Treatment of anxiety disorders in patients with comorbid bipolar disorder
Bibliographic record
Abstract
Anxiety disorders are the most prevalent comorbid diagnoses in patients with bipolar disorder (BD). A comorbid anxiety diagnosis can significantly impact the severity of bipolar symptoms, increase the risk of suicidality, and decrease psychosocial functioning and quality of life. The Canadian Network for Mood and Anxiety Treatments (CANMAT) task force published recommendations for treatment in 2012 suggesting that specific anticonvulsant mood stabilizers and second-generation antipsychotics are the medications of choice to treat these comorbidities. Serotonergic antidepressant medications are first-line medications for the treatment of most anxiety disorders; however, this can be problematic for a patient with BD. Antidepressant use in BD has been associated with a risk of manic switch as well as potential destabilization of mood. Mood stabilizer therapy should be established for patients with comorbid BD and an anxiety disorder before other medications are added to address the anxiety disorder. While benzodiazepine medications are recommended as third-line therapy in the CANMAT task force recommendations, their use should be avoided in patients with comorbid BD, posttraumatic stress disorder, and substance use disorders. The use of benzodiazepines should in general be avoided for all patients if possible, based upon current clinical research. Interpersonal, cognitive behavioral, and relaxation therapy are effective for the treatment of anxiety symptoms, especially emotional experiences, in patients who are euthymic.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".