C-reactive protein and cholesterol are equally strong predictors of cardiovascular risk and both are important for quality clinical care
Bibliographic record
Abstract
Physicians do not measure biomarkers simply to predict risk. Rather, they do so to better target therapy and improve the lives of their patients. Thus, when considering the use of any biomarker for cardiovascular risk prediction in primary prevention, thoughtful clinicians, and those writing guidelines should insist that two fundamental questions be answered affirmatively. First, is there clear evidence that the biomarker of interest predicts future cardiovascular events independent of other risk markers? And secondly, is there clear evidence that those identified by the biomarker of interest benefit from a therapy they otherwise would not have received? No imaging biomarker can answer these questions affirmatively, nor can a variety of plasma biomarkers such as lipoprotein(a) or triglycerides. As we will discuss below, the answer to both of these questions is clearly ‘yes’ for C-reactive protein as well as for cholesterol. Yet, while recent European Society of Cardiology guidelines for the prevention of heart disease strongly endorse cholesterol screening, those same guidelines are silent on C-reactive protein.1 Inflammation is a fundamental component of atherosclerosis.2 For more than a decade, data from large-scale prospective cohorts in the USA and Europe have consistently indicated that the predictive value of the inflammatory biomarker C-reactive protein is at least as large as that of cholesterol.3,4 This observation is important since half of all heart attacks and strokes occur among those with average if not low cholesterol levels. That C-reactive protein and lipids are equal contributors to vascular risk has recently been confirmed in an elegant 2012 meta-analysis published in the New England Journal of Medicine by the Emerging Risk Factors Collaboration that analysed data from 38 prospective studies and included 166 596 men and women without prior disease.5 Specifically, for a prediction model that included age, smoking, systolic blood …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".