Medical comorbidity in bipolar disorder: reprioritizing unmet needs
Bibliographic record
Abstract
PURPOSE OF REVIEW: The aim of this review is to synthesize results from extant investigations which report on the co-occurrence of bipolar disorder and medical comorbidity. RECENT FINDINGS: We conducted a MEDLINE search of all English-language articles published between January 2004 and November 2006. Most studies report on medical comorbidity in bipolar samples; relatively fewer studies report the reciprocal association. Individuals with bipolar disorder are differentially affected by several 'stress-sensitive' medical disorders notably circulatory disorders, obesity and diabetes mellitus. Neurological disorders (e.g. migraine), respiratory disorders and infectious diseases are also prevalent. Although relatively few studies have scrutinized the co-occurrence of bipolar disorder in medical settings, individuals with epilepsy, multiple sclerosis, migraine and circulatory disorders may have a higher prevalence of bipolar disorder. A clustering of traditional and emerging (e.g. immuno-inflammatory activation) risk factors presage somatic health issues in the bipolar disorder population. Iatrogenic factors and insufficient access to primary, preventive and integrated healthcare systems are also contributory. SUMMARY: Somatic health issues in individuals with bipolar disorder are ubiquitous, under-recognized and suboptimally treated. Facile screening for risk factors and laboratory abnormalities along with behavioral modification for reducing medical comorbidity are warranted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".