Abstract TP131: Carotid Stenosis and Polyvascular Disease
Bibliographic record
Abstract
Background: Presence of atherosclerotic disease in more than one vascular bed (polyvascular disease) is associated with an increased risk of long-term vascular events. In the setting of asymptomatic carotid stenosis, the predictors of disease in other vascular beds is not well delineated. Objective: To identify the frequency and predictors of disease in other vascular beds the CREST 2 randomized trial population with hemodynamically significant stenosis. Methods: Recorded baseline characteristics among CREST 2 participants included: demographics, past medical history, lifestyle factors, and laboratory studies. Each variable was tested against three potential definitions of disease in other beds (cardiac, peripheral arterial disease (PAD) or disease in cardiac + PAD). Statistical analysis was done with Chi square and t tests as appropriate. Results: Data from 1447 patients were available for analysis. In these subjects with carotid disease, 51% also had cardiac disease, 24% also had PAD, and 16% had disease in in both cardiac and PAD. There was no relationship between age or race and presence of disease in other beds. Those with cardiac disease were more likely to be male. Diabetes, hypertension, hypercholesterolemia, and former history of smoking were all more common in those with either cardiac and PAD (TABLE). Elevated triglyceride and low HDL levels were also associated with all three definitions of disease in other beds. Those with either PAD or cardiac + PAD were more likely to be current smokers. Conclusions: Among CREST 2 participants, several medical conditions and lifestyle factors were associated with an increased frequency of disease in other vascular beds. Smoking, in particular, appears to be more common in those with PAD. Future analyses will address whether those with disease in other beds have higher rates of stroke or death.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.009 | 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".