C-Reactive Protein and High-Sensitivity C-Reactive Protein: An Update for Clinicians
Bibliographic record
Abstract
The measurement of C-reactive protein (CRP) using both standard and high-sensitivity CRP (hs-CRP) assays is becoming common in clinical practice. This article addresses the causes of CRP elevation and the use of different CRP assays in internal medicine, including cardiology, gastroenterology, rheumatology, infectious diseases, and oncology. We focus on the recent medical literature on the use of hs-CRP in cardiovascular disease risk stratification and management, including updated screening guidelines on the use of hs-CRP, such as those issued in 2009 by the Canadian Cardiovascular Society. We also discuss the Reynolds Risk Score, which incorporates hs-CRP and family history with more standard cardiovascular risk factors (eg, tobacco use, hypertension, and dyslipidemia) and frequently leads to improved recategorization of cardiovascular disease risk levels. As the recently completed Justification for the Use of Statins in Prevention: An Intervention Trial Evaluating Rosuvastatin (JUPITER) trial indicated that statin therapy decreases the vascular events among persons with elevated hs-CRP by half, even when cholesterol levels are low, the inclusion of information on hs-CRP values with other cardiovascular risk factors may assist physicians in medical decision making for patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".