The Quest for the Optimal Assessment of Global Cardiovascular Risk: Are Traditional Risk Factors and Metabolic Syndrome Partners in Crime?
Bibliographic record
Abstract
Global risk calculators such as the Framingham risk score generally take into account traditional risk factors such as age, sex, blood pressure, smoking status, total cholesterol and high-density lipoprotein cholesterol levels, and the presence of diabetes which are recommended to be used in clinical practice to estimate patients' cardiovascular disease (CVD) risk. Over the last decades, the prevalence of obesity has dramatically increased all over the world. The deleterious form of obesity, visceral obesity, is the most prevalent form of the so-called metabolic syndrome, a constellation of risk factors associated with perturbations of the lipoprotein-lipid profile and of the plasma glucose-insulin homeostasis also likely to be associated with increased blood pressure and a proinflammatory and prothrombotic state. To this date, metabolic syndrome is still in need of a place in global CVD risk prediction. As the metabolic syndrome is not likely to replace currently used global risk scoring algorithms, both traditional risk factors and emerging metabolic markers associated with the metabolic syndrome should be incorporated in future risk scoring systems to be developed in order to adapt CVD risk prediction tools to the epidemic of obesity which has direct consequences on the daily life of health professionals.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".