Modified Framingham Risk Factor Score for Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: The traditional Framingham Risk Factor Score (FRS) underestimates the risk for coronary artery disease (CAD) in patients with systemic lupus erythematosus (SLE). We aimed to determine whether an adjustment to the FRS would more accurately reflect the higher prevalence of CAD among patients with SLE. METHODS: Patients with SLE without a previous history of CAD or diabetes followed regularly at the University of Toronto Lupus Clinic were included. A modified FRS (mFRS) was calculated by multiplying the items by 1.5, 2, 3, or 4. In the first part of the study, using one-third of all eligible patients, we evaluated the sensitivity and specificity of the FRS and the different multipliers for the mFRS. In the second part of the study, using the remaining 2/3 of the eligible patients, we compared the predictive ability of the FRS to the mFRS. In the third part of the study, we assessed the prediction for CAD in a time-dependent analysis of the FRS and mFRS. RESULTS: There were 905 women (89.3%) with a total of 95 CAD events included. In part 1, we determined that a multiplier of 2 provided the best combination of sensitivity and specificity. In part 2, 2.4% of the patients were classified as moderate/high risk based on the classic FRS and 17.3% using the 2FRS (the FRS with a multiplier of 2). In part 3, a time-dependent covariate analysis for the prediction of the first CAD event revealed an HR of 3.22 (p = 0.07) for the classic FRS and 4.37 (p < 0.0001) for the 2FRS. CONCLUSION: An mFRS in which each item is multiplied by 2 more accurately predicts CAD in patients with SLE.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".