Appropriate cardiovascular disease risk assessment in systemic lupus erythematosus may be lacking in rheumatology practice.
Bibliographic record
Abstract
OBJECTIVES: To determine practices regarding cardiovascular (CV) risk assessment in systemic lupus erythematosus (SLE) amongst rheumatologists. METHODS: A questionnaire assessing preventative strategies, risk assessment, and beliefs regarding SLE and CV disease was sent electronically to 425 members of the Canadian Rheumatology Association. Questions were based on published recommendations for CV risk management. Responses were stratified based practitioner's characteristics. RESULTS: Ninety-nine rheumatologists and trainees responded (22% response rate). Nearly all (91%) believed that SLE is a major CV risk factor, and 68% felt rheumatologists should assess CV risk; whereas 42% were not comfortable with guidelines, 97% felt that family physicians are not aware of the CV risk in SLE but 64% did not routinely inform them in their correspondence. For SLE patients followed: 15% did not check blood pressure at every visit, 32% did not order cholesterol and 34% did not screen for diabetes irrespective of the presence of additional risk factors. Half (54%) would stratify SLE patients as intermediate or high risk when deciding on lipid lowering treatment. For SLE, 45% recommended a target blood pressure of 140/90 and 55% recommended 130/80 as the target. CONCLUSIONS: CV risk assessment and preventative measures were inconsistent when rheumatologists monitored SLE patients, indicating a care gap. Improved communication between rheumatologists and family physicians with respect to elevated CVD risk in SLE is needed.
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".