Assessment of novel risk factors in patients at low risk for cardiovascular events based on Framingham risk stratification.
Bibliographic record
Abstract
BACKGROUND: Coronary heart disease (CHD) risk assessed by the Framingham risk score does not take into account the various "novel" markers that are of increasing interest. In this paper we examine a low-risk population to determine which novel markers may be of additive value to the Framingham assessment of CHD risk. METHODS: Levels of high-sensitivity C-reactive protein (hs-CRP), soluble vascular cell adhesion molecule (s-VCAM), soluble intercellular adhesion molecule (s-ICAM-1), endothelial selectin (e-selectin), homocysteine and von Willebrand factor (vWF) were measured in 53 apparently healthy subjects recruited from a risk-reduction referral clinic. Carotid intima medial thickness (IMT) and number of plaques were determined by ultrasonography. Brachial ultrasound flow-mediated dilation (FMD) was also measured. Framingham risk scores were calculated and univariate and multivariate analyses of the resulting percent CHD risk over 10 years and novel markers were undertaken. RESULTS: Abnormal carotid IMT and presence of plaques, hs-CRP, homocysteine, FMD and s-ICAM-1 were detected with a high frequency in this low-risk cohort. Average IMT, number of plaques and homocysteine were highly correlated with the calculated percent CHD whereas measures of hs-CRP, s-ICAM-1 and FMD were independent of the percent CHD calculation. CONCLUSIONS: FMD, as a reflection of the functional status of the vasculature, and hs-CRP and s-ICAM-1, as indicators of inflammatory processes, were independent of Framingham risk assessment in patients at low risk for cardiovascular disease.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".