Knee and hip osteoarthritis increase the risk of cardiovascular disease: A national registry-based longitudinal cohort study
Bibliographic record
Abstract
OBJECTIVE: Osteoarthritis and cardiovascular disease are major public health challenges. We aimed to estimate the average sex-specific effects of knee and hip osteoarthritis on the risk of cardiovascular disease. METHODS: We used 2001-2015 Danish national health registry data to identify all adults with knee or hip osteoarthritis and an age-, sex-, and education-matched group without osteoarthritis. Cardiovascular disease outcomes were identified with relevant ICD-10 codes. The effects of osteoarthritis were estimated with sex-stratified multivariable Cox regression models, accounting for multiple sources of confounding determined a priori with a directed acyclic graph. Results were reported with cumulative incidence curves and hazard ratios (HR) conditioned on age, sex, education, and obesity diagnosis. Sensitivity analyses explored the potential impacts of bias owing to outcome misclassification and unmeasured confounding. RESULTS: We analysed data from 1,838,434 adults, including 290,781 people with knee or hip osteoarthritis and 1,547,653 age-, sex-, and education-matched controls. Women with knee or hip osteoarthritis had a 44% increased hazard of cardiovascular disease (HR [95% CI] = 1.44 [1.43 to 1.46]), while men with knee or hip osteoarthritis had a 24% increased hazard of subsequent cardiovascular disease (HR[95% CI] = 1.24 [1.23 to 1.26]) compared to people without osteoarthritis. These results were confirmed by sensitivity analyses. CONCLUSION: The apparent effect of osteoarthritis on cardiovascular disease was stronger in women than in men. Clinicians who care for patients with osteoarthritis should be aware of cardiovascular disease risk when selecting therapies and consider behavioural approaches to improving health-related physical activity behaviour in this population.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".