Cardiovascular risk management in rheumatoid arthritis: A large gap to close
Bibliographic record
Abstract
OBJECTIVE: Rheumatoid arthritis (RA) portends significant cardiovascular morbidity and mortality. We therefore determined how often rheumatologists screened for and managed cardiovascular risk factors in RA patients, and the barriers to doing so. METHODS: We examined 300 patient charts from 10 university-affiliated rheumatology practices, to ascertain if they had been screened, treated and/or referred over a 3-year period. We subsequently distributed a national survey to Canadian rheumatologists to elucidate challenges in performing optimal cardiovascular risk modification. RESULTS: Most patients were screened for hypertension. Forty-one per cent were found to be hypertensive; however, the majority of these patients were neither treated nor referred to another provider for management. A small minority of patients were screened for diabetes and/or hyperlipidaemia, and these were usually not addressed if abnormal. Men were referred more frequently than women. Consistent with these findings, the majority of rheumatologists from the national survey felt that they did not manage cardiovascular risk adequately; 79.4% cited a lack of time as a major barrier, and 82.5% felt that it should be managed by the primary care provider. CONCLUSION: There is marked underdiagnosis and undertreatment of cardiac risk in RA. Several major barriers exist, including lack of time. Most rheumatologists feel that this aspect of care is the responsibility of primary care physicians.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".