Best Practices for Cardiovascular Disease Prevention in Rheumatoid Arthritis: A Systematic Review of Guideline Recommendations and Quality Indicators
Bibliographic record
Abstract
OBJECTIVE: Cardiovascular disease (CVD) is a leading cause of mortality in rheumatoid arthritis (RA). This study systematically reviewed and appraised guidelines and quality indicators (QIs) pertaining to CVD risk management in patients with RA. METHODS: Four electronic medical databases (Medline, Embase, CINAHL, and Web of Science) and gray literature publications were searched using terms and keywords pertaining to guidelines, QIs, RA, and CVD (RA and general population literature searched). Abstracts were screened for inclusion and rated using the Appraisal of Guidelines for Research and Evaluation II instrument independently by 2 of 3 reviewers. RESULTS: In total, 16,064 abstracts were screened and 808 underwent full-text review. A total of 17 guidelines and 3 QI sets published between 2008 and 2013 were included. A number of consistent themes emerged, including the increased CV risk faced by RA patients and the need to address modifiable risk factors on a regular basis. The role of the multidisciplinary team in risk optimization was also highlighted. Ten guidelines provided recommendations for CVD prevention in patients with RA. Unfortunately, most recommendations lacked the specificity required to determine adherence to the recommendation. Only 4 RA-specific CVD QIs were identified (1 general comorbidity screening, formal CVD risk estimation, exercise, and minimizing steroid use). CONCLUSION: Regular screening for CVD risk factors is an important part of care in patients with RA. Unfortunately, existing RA-specific CVD QIs do not adequately address risk factor management, and existing guideline recommendations lack specificity for measurement and use in quality improvement initiatives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".