ASSESSMENT OF ATHEROSCLEROSIS RISK IN LUPUS: A COMPARISION OF CLINICAL ALGORITHMS AND CAROTID ULTRASOUND
Bibliographic record
Abstract
PV049 / #291 Poster Topic: AS06 - Comorbidities Background/Purpose Systemic Lupus Erythematosus (SLE) patients have twice the incidence of cardiovascular diseases (CVD) than the general population. Traditional factors (obesity, smoking, dyslipidemia) do not fully explain the accelerated rate of atherosclerosis and cardiovascular disease in patients with SLE. Carotid Ultrasound (CU) is a surrogate marker for atherosclerotic CVD. Methods We included consecutive SLE patients with 18 years or older. Calculation of several algorithms to assess cardiovascular risk (Fragminham, SCORE, QRISK3, mSCORE and mFragminham) and CU with measurement of carotid intima-media thickening (CIMT) and evaluation of presence of plaques. In addition, disease-related variables and traditional CV risk factors were reviewed. Statistic was done according to nature of the variables, p values <0.05 were considered statistically significant, after adjusting fr multiple comparisons. Considering plaque seen on ultrasound as a gold standard, sensibility/specificity of each clinical score was calculated. Results We included 159 SLE patients [median age 51.1years; 149 (93.7%) women]. Thirty-two (20.1%) patients presented atherosclerotic plaques on CU and altered CIMT was observed in 141 (88%) patients. All of the clinical scores and traditional CV risk factors had statistical significance in patients with plaques (Table 1). Traditional and disease-linked factors were associated with clinical scores positivity (Table 2). When using the presence of plaques as gold standard of atherosclerosis, SCORE and Mscore had the highest sensitivity, however all clinical scores had a poor accuracy, ranging of 17.7-31.2 (Table 3). Table 1. Statistical significance of Clinical and algorithms variables in patients with established plaques Table 2. Relevant clinical traits associated with each score (p <0,05) Table 3. Characteristics of each clinical score Conclusions Clinical scores failed to predict the presence of carotid atherosclerotic disease as seen on ultrasound. Mscore is the most accurate clinical score in this study. Longitudinal studies are needed to show the interaction between traditional and disease-linked factors.
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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 | 0.029 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".