Is arthritis associated with cardiovascular diseases in both sexes? An analysis of NHANES data
Bibliographic record
Abstract
Background: Among arthritis patients, cardiovascular morbidity is increased, and sex disparities exist in cardiovascular diseases and arthritis.Thus, the hypothesis that the association between cardiovascular diseases and arthritis might vary according to sex seems plausible.Aim: To examine the relationship of arthritis with the most frequently diagnosed cardiovascular diseases, and to check if sex modifies this association. Methods:We analyzed data from a large representative sample of the U.S. population: The National Health and Nutrition Examination Survey (NHANES) 2005-2006.Our cross-sectional analyses included 3621 participants aged between 20 and 69 years old.Self-reported diagnoses of arthritis (main independent variable) and cardiovascular diseases (dependent variable) were measured during face-to-face interviews, and different covariates were identified as potential confounders for the association examined.Analyses were stratified by sex.Results: In multivariate analyses, a significant positive association was found between arthritis and cardiovascular diseases (OR: 2.01; 95% CI: 1.38-2.94;p value=0.0003), but there was no effect modification by sex (men: OR: 1.95; 95% CI: 1.32-2.87/ women: OR: 2.38; 95% CI: 0.96-5.86)(p=0,729). Conclusions:In this large cross-sectional study, we observed a statistically significant association between self-reported diagnoses of arthritis and cardiovascular diseases in a cohort of non-institutionalized U.S. adults.The association persisted after adjustment for several confounders but was not modified by sex.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".