The Use of High Sensitivity C-Reactive Protein in Cardiovascular Disease Detection
Bibliographic record
Abstract
Cardiovascular diseases (CVDs) are responsible for a high mortality rate worldwide. One of the most common causes of CVDs is vascular inflammation associated to atherosclerosis. Inflammatory biomarkers are used to assist the detection of CVDs and monitor their evaluation, prognosis and therapy implementation. C-reactive protein (CRP) is an acute phase protein produced after stimulation by pro-inflammatory cytokines. CRP is a biomarker of the inflammatory reaction and an important mediator of atherosclerosis. Given it actively contributes to the development of the atherosclerotic plaque, instability and subsequent clot formation it is also considered a CVD risk factor. Since 2010, the plasma concentration of hsCRP (high sensitivity CRP) has been used as a biomarker for disease prognosis in patients with intermediate risk for CVDs. It could be useful to establish a high concentration limit of hsCRP that can be used by clinicians for diagnosis of acute myocardial infarction, cardio embolic or ischemic stroke, and hypertrophic cardiomyopathy. The end cost/effectiveness of hsCRP screening is still an area of controversy but it is a priority to make the medical community aware of the positive relation between high hsCRP and CVDs to improve median survival and life quality of the patients.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".