Gaps in Addressing Cardiovascular Risk in Rheumatoid Arthritis: Assessing Performance Using Cardiovascular Quality Indicators
Bibliographic record
Abstract
OBJECTIVE: Cardiovascular disease (CVD) is a major comorbidity for patients with rheumatoid arthritis (RA). This study sought to determine the performance of 11 recently developed CVD quality indicators (QI) for RA in clinical practice. METHODS: Medical charts for patients with RA (early disease or biologic-treated) followed at 1 center were retrospectively reviewed. A systematic assessment of adherence to 11 QI over a 2-year period was completed. Performance on the QI was reported as a percentage pass rate. RESULTS: There were 170 charts reviewed (107 early disease and 63 biologic-treated). The most frequent CVD risk factors present at diagnosis (early disease) and biologic start (biologic-treated) included hypertension (26%), obesity (25%), smoking (21%), and dyslipidemia (15%). Performance on the CVD QI was highly variable. Areas of low performance (< 10% pass rates) included documentation of a formal CVD risk assessment, communication to the primary care physician (PCP) that patients with RA were at increased risk of CVD, body mass index documentation and counseling if overweight, communication to a PCP about an elevated blood pressure, and discussion of risks and benefits of antiinflammatories in patients at CVD risk. Rates of diabetes screening and lipid screening were 67% and 69%, respectively. The area of highest performance was observed for documentation of intent to taper corticosteroids (98%-100% for yrs 1 and 2, respectively). CONCLUSION: Gaps in CVD risk management were found and highlight the need for quality improvements. Key targets for improvement include coordination of CVD care between rheumatology and primary care, and communication of increased CVD risk in RA.
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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 | 0.026 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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 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".