The Framingham Score and the Systematic Coronary Risk Evaluation at Low Cutoff Values Are Useful Surrogate Markers of High-risk Subclinical Atherosclerosis in Patients with Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: We determined the performance of the Framingham score and the Systematic COronary Risk Evaluation (SCORE) in assessing high-risk atherosclerosis in patients with rheumatoid arthritis (RA). METHODS: We assembled 330 cases without established cardiovascular disease (CVD), diabetes, and moderate or severe chronic kidney disease among 451 consecutive Spanish patients who underwent CVD risk screening and carotid ultrasound-determined plaque assessment. The findings were validated in 90 black and 97 white African patients. RESULTS: When sensitivity for the Framingham score was set at 80% in receiver-operator curve analysis [area under the curve (AUC) = 0.799], the corresponding cutoff value and specificity were 7.3% and 63%, respectively. At a specificity of 80%, the cutoff value and sensitivity were 10.8% and 65%, respectively. When sensitivity for SCORE (AUC = 0.747) was set at 80%, the cutoff value and specificity were 0.5% and 58%, respectively. At a specificity of 80%, the cutoff value and sensitivity were 1.5% and 50%, respectively. Upon applying a cutoff value of 7.3% for the Framingham and 0.5% for SCORE in African white patients with RA, the corresponding sensitivities and specificities were 67% and 72%, and 67% and 55%, respectively. CVD risk equations did not discriminate between black African patients with and without plaque (AUC = 0.544 and 0.549 for Framingham score and SCORE, respectively). CONCLUSION: The Framingham score and SCORE at markedly low cutoff values of 7.3% to 10.8% and 0.5% to 1.5%, respectively, can usefully estimate plaque presence in RA. Effective population-specific CVD risk assessment strategies are needed in black African patients with 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 | 0.004 | 0.013 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".