Toward Quality of Cardiovascular Preventive Care for Patients with Rheumatic Diseases
Bibliographic record
Abstract
Cardiovascular disease (CVD) in various guises represents perhaps the most important comorbidity in patients with rheumatic diseases. This is best studied in patients with rheumatoid arthritis (RA), who have consistently been reported to have significantly elevated CVD risk as compared to their peers in the general population1,2. Over the past decade, the roles of RA-associated and conventional risk factors in the pathogenesis of CVD have been delineated3. The importance of treating the joint disease to target has been established, focusing on abating systemic inflammation, which may conceivably minimize the risk of CVD4,5. Emerging evidence suggests that we may be contributing to improved survival of patients with RA, but evidence remains of a persisting mortality gap between patients with RA and the general population5,6,7,8. Considering this information, it should be considered a priority to establish standards of preventive care that are specific to patients with RA (and eventually, other rheumatic diseases). Indeed, there is evidence pointing to variation in practice and to deficiencies in recognition and treatment of traditional CV risk factors in patients with RA8,9. The ultimate goal of developing RA-specific guidelines for CVD primary prevention would be to facilitate broader understanding of best practices and provide metrics for rheumatology care providers to ensure they are delivering high-value care toward primary prevention of CVD in patients with RA. In this issue of The Journal , Barber and colleagues report recommendations for 11 quality indicators (QI) for CVD preventive care for implementation in practice settings10. The concept of … Address correspondence to Dr. Davis, Mayo Building 15-73E, 200 1st St. SW, Rochester, Minnesota 55905, USA. E-mail: davis.john4{at}mayo.edu
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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 | 0.068 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".