Cardiovascular risk factor screening in systemic lupus erythematosus.
Bibliographic record
Abstract
OBJECTIVE: To evaluate the frequency of cardiovascular (CV) disease risk factor screening in systemic lupus erythematosus (SLE). METHODS: Medical records of patients from a lupus clinic and 5 private practices were assessed for CV disease risk factors, including hyperlipidemia, hypertension, diabetes mellitus, smoking, family history of CV disease, antiphospholipid antibodies, hyperhomocysteinemia, postmenopausal status, obesity, and nephrotic syndrome. RESULTS: A total of 183 records were included: 60 (33%) from the lupus clinic and 123 (67%) from private practices. Serum lipid profiles were measured in 56/183 (31%): 37/60 (62%) in the lupus clinic vs 19/123 (15%) private practice. Of the 56 with lipids measured, the individual tests obtained were as follows: total cholesterol in 56 (100%), HDL in 50 (89%), triglycerides in 49 (88%), LDL in 48 (86%), and VLDL in 33 (59%). Thirty-one of 56 patients (55%) had elevated lipids. Only 9/25 (36%) with hyperlipidemia who had a subsequent visit had a response to the hyperlipidemia charted. Of 9 nonlipid risk factors, a median of 8 were assessed in the lupus clinic vs 3 in private practices. The most frequent risk factors screened were nephrotic syndrome (91%), hypertension (74%), and smoking (59%). CONCLUSION: Despite an inordinately high risk of CV disease in SLE, assessment of CV risk factors was surprisingly uncommon among the practices assessed. Greater attention needs to be paid to CV disease risk factor screening in patients with lupus.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".