Common Comorbidities of Stroke in the Canadian Population
Bibliographic record
Abstract
OBJECTIVES: Although comorbidity increases the health care and community support needs for patients, and the burden for the health care system, there are few population-based studies on comorbidity in patients with stroke. This study aims to evaluate the occurrence of important comorbidities among stroke patients in the Canadian population. METHODS: Data from the population-based 2011-2012 Canadian Community Health Survey containing responses from 124,929 participants covering about 98% of the Canadian population when weighted were examined and analyzed by means of logistic regression models. RESULTS: There was a statistically significant association between stroke history and multiple comorbid risk factors. Stroke prevalence increased in individuals with heart disease (odds ratio (OR): 3.80, 95% confidence interval (CI): 3.77-3.84), hypertension (OR: 1.97, 95% CI: 1.95-1.99), diabetes (OR: 1.74, 95% CI: 1.72-1.75), mood disorder (OR: 2.14, 95% CI: 2.12-2.17), and chronic obstructive pulmonary disease (COPD) (OR: 1.46, 95% CI: 1.44-1.48) compared to others without the condition. Of 2067 participants with stroke, 1680 (81.3%) had one or more comorbid conditions (heart disease, hypertension, diabetes, mood disorder, or COPD) that coexist with stroke and 48% had two or more. Comorbidity increased with age, and two-thirds of stroke patients with comorbid medical conditions were 60 years of age or older. CONCLUSION: This population-based study provides evidence of comorbidity between stroke and other conditions that include heart disease, hypertension, diabetes, mood disorder, and COPD. Canadian individuals with stroke have a high burden of comorbidity. Health care systems need to recognize and respond to the strong association of comorbidity and stroke occurrence. This key factor should be considered when allocating resources.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".