Prevalence of current anxiety disorders in people with bipolar disorder during euthymia: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: Anxiety disorders are highly prevalent in people with bipolar disorder, but it is not clear how many have anxiety disorders even at times when they are free of major mood episodes. We aimed to establish what proportion of euthymic individuals with bipolar disorder meet diagnostic criteria for anxiety disorders. METHOD: We performed a random-effects meta-analysis of prevalence rates of current DSM-III- and DSM-IV-defined anxiety disorders (panic disorder, agoraphobia, social anxiety disorder, generalized anxiety disorder, specific phobia, obsessive-compulsive disorder, post-traumatic stress disorder, and anxiety disorder not otherwise specified) in euthymic adults with bipolar disorder in studies published by 31 December 2015. RESULTS: Across 10 samples with 2120 individuals with bipolar disorder, 34.7% met diagnostic criteria for one or more anxiety disorders during euthymia [95% confidence interval (CI) 23.9-45.5%]. Direct comparison of 189 euthymic individuals with bipolar disorder and 17 109 population controls across three studies showed a 4.6-fold increase (risk ratio 4.60, 95% CI 2.37-8.92, p < 0.001) in prevalence of anxiety disorders in those with bipolar disorder. CONCLUSIONS: These findings suggest that anxiety disorders are common in people with bipolar disorder even when their mood is adequately controlled. Euthymic people with bipolar disorder should be routinely assessed for anxiety disorders and anxiety-focused treatment should be initiated if indicated.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.055 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".