Development of Cardiovascular Quality Indicators for Rheumatoid Arthritis: Results from an International Expert Panel Using a Novel Online Process
Bibliographic record
Abstract
OBJECTIVE: Patients with rheumatoid arthritis (RA) have a high risk of premature cardiovascular disease (CVD). We developed CVD quality indicators (QI) for screening and use in rheumatology clinics. METHODS: A systematic review was conducted of the literature on CVD risk reduction in RA and the general population. Based on the best practices identified from this review, a draft set of 12 candidate QI were presented to a Canadian panel of rheumatologists and cardiologists (n = 6) from 3 academic centers to achieve consensus on the QI specifications. The resulting 11 QI were then evaluated by an online modified-Delphi panel of multidisciplinary health professionals and patients (n = 43) to determine their relevance, validity, and feasibility in 3 rounds of online voting and threaded discussion using a modified RAND/University of California, Los Angeles Appropriateness Methodology. RESULTS: Response rates for the online panel were 86%. All 11 QI were rated as highly relevant, valid, and feasible (median rating ≥ 7 on a 1-9 scale), with no significant disagreement. The final QI set addresses the following themes: communication to primary care about increased CV risk in RA; CV risk assessment; defining smoking status and providing cessation counseling; screening and addressing hypertension, dyslipidemia, and diabetes; exercise recommendations; body mass index screening and lifestyle counseling; minimizing corticosteroid use; and communicating to patients at high risk of CVD about the risks/benefits of nonsteroidal antiinflammatory drugs. CONCLUSION: Eleven QI for CVD care in patients with RA have been developed and are rated as highly relevant, valid, and feasible by an international multidisciplinary panel.
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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 | 0.301 | 0.249 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".