Association between Cardiovascular Diseases and Knee Osteoarthritis
Bibliographic record
Abstract
CONTEXT: Cardiovascular diseases (CVDs) such as ischemic heart diseases, heart failure, and stroke are the leading causes of morbidity and mortality (almost 30% of deaths) worldwide. Sociodemographic and clinical factors, such as obesity, diabetes, depressive symptoms, and physical inactivity, as factors behind the risk of CVDs. AIMS: This study aims to identify the factors behind the risk of CVDs in people with or at high risk for Knee Osteoarthritis (OA). SETTINGS & DESIGN: The baseline data (2004–2006) of a total of 4674 persons with or at high risk for knee OA aged 45–79 years from the Osteoarthritis Initiative (OAI). METHODS & MATERIAL: This study adopted a cross-sectional study. Baseline data (2004–2006) from the Osteoarthritis Initiative were analyzed to determine the sociodemographic and clinical factors behind CVDs in 4674 persons. STATISTICAL ANALYSIS USED: The Kolmogorov-Smirnov test was used to assess the data normality for continuous parameters. RESULTS: The results indicate 178 (62%) participants with age ≥65 years also had CVDs (p <.0001). Male gender had OR = 2.97 for heart attack and OR = 2.53 for heart failure making the implied probability of 33.7% and 39.5% respectively (p < 0.05). The OR and implied probability of diabetes and obesity for heart failure were 1.81 (55.2%) and 2.20 (45.5%) respectively (p<0.05). CONCLUSIONS: These findings provide a rationale for further investigation of those factors behind the risk of CVDs in cross-sectional studies among 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".