Medical Comorbidity in Bipolar Disorder: Implications for Functional Outcomes and Health Service Utilization
Bibliographic record
Abstract
OBJECTIVE: This is the first cross-national population-based investigation exploring the prevalence and functional implications of comorbid general medical disorders in bipolar disorder. METHODS: Data were extracted from the Canadian Community Health Survey (N = 36,984). Analyses were conducted to ascertain the prevalence and prognostic implications of predetermined comorbid general medical disorders among persons who screened positive for a lifetime manic episode (indicative of a diagnosis of bipolar disorder). Within the subpopulation of people who screened positive for a manic episode, the effect of medical comorbidity on employment, functional role, psychiatric care, and medication use was examined. RESULTS: When the data were weighted to be representative of the household population of the ten provinces in 2002, an estimated 2.4 percent of respondents screened positive for a lifetime manic episode. Rates of chronic fatigue syndrome, migraine, asthma, chronic bronchitis, multiple chemical sensitivities, hypertension, and gastric ulcer were significantly higher in the bipolar disorder group (all p < .05). Chronic medical disorders were associated with a more severe course of bipolar disorder, increased household and work maladjustment, receipt of disability payments, reduced employment, and more frequent medical service utilization. CONCLUSIONS: Comorbid medical disorders in bipolar disorder are associated with several indices of harmful dysfunction, decrements in functional outcomes, and increased utilization of medical services.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".