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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| 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) | 0.003 | 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".