Traditional Framingham risk factors fail to fully account for accelerated atherosclerosis in systemic lupus erythematosus
Bibliographic record
Abstract
OBJECTIVE: The frequency of coronary heart disease (CHD) and stroke are increased in systemic lupus erythematosus (SLE), but the extent of the increase is uncertain. We sought to determine to what extent the increase could not be explained by common risk factors. METHODS: The participants at two SLE registries were assessed retrospectively for the baseline level of the Framingham study risk factors and for the presence of vascular outcomes: nonfatal myocardial infarction (MI), death due to CHD, overall CHD (nonfatal MI, death due to CHD, angina pectoris, and congestive heart failure due to CHD), and stroke. For each patient, the probability of the given outcome was estimated based on the individual's risk profile and the Framingham multiple logistic regression model, corrected for observed followup. Ninety-five percent confidence intervals (95% CIs) were estimated by bootstrap techniques. RESULTS: Of 296 SLE patients, 33 with a vascular event prior to baseline were excluded. Of the 263 remaining patients, 34 had CHD events (17 nonfatal MIs, 12 CHD deaths) and 16 had strokes over a mean followup period of 8.6 years. After controlling for common risk factors at baseline, the increase in relative risk for these outcomes was 10.1 for nonfatal MI (95% CI 5.8-15.6), 17.0 for death due to CHD (95% CI 8.1-29.7), 7.5 for overall CHD (95% CI 5.1-10.4), and 7.9 for stroke (95% CI 4.0-13.6). CONCLUSION: There is a substantial and statistically significant increase in CHD and stroke in SLE that cannot be fully explained by traditional Framingham risk factors alone.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".