Quality of care for cardiovascular disease prevention in rheumatoid arthritis: compliance with hyperlipidemia screening guidelines
Bibliographic record
Abstract
Objective: To evaluate compliance with hyperlipidaemia screening guidelines for cardiovascular disease prevention in RA compared with the general population. Methods: We conducted a longitudinal study of a population-based RA cohort including all prevalent cases in British Columbia between 1996 and 2006, followed up until 2010, with matched general population controls. Using administrative data, we measured compliance with general population guidelines (testing lipids every 5 years for women ⩾50 and men ⩾40), after excluding individuals with previous diabetes, coronary artery disease or hyperlipidaemia. Compliance was measured as the proportion of 5-year eligibility periods with one or more lipid test. Compliance rates in RA and controls were compared by Chi-square test. Odds ratio (95% CI) of compliance in RA (vs controls) was estimated using generalized estimating equation models, adjusting for age and sex. Mean compliance rate per patient was also calculated and compared using Mann-Whitney U test. Results: Analyses included 5587 RA individuals and 5613 controls, contributing 6993 and 7208 5-year eligibility periods, respectively. Lipids were measured in 56.6 and 59.5% of eligibility periods in RA and controls, respectively [adjusted odds ratio (95% CI): 0.97 (0.90, 1.06)]. Screening improved over time in RA relative to the general population, but remained suboptimal even after 2003, at 65.8%. Mean (s.d.) compliance rate per patient was 56.6 (47.2)% for RA and 59.5 (46.6)% for controls. Family physicians ordered almost all the lipid tests. Conclusion: Compliance with general population guidelines for hyperlipidaemia screening in RA was poor and did not differ from the general population, despite a higher risk of cardiovascular diseases.
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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 | 0.008 | 0.026 |
| 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 | 0.001 | 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".