Is it important to measure or reduce C-reactive protein in people at risk of cardiovascular disease?
Bibliographic record
Abstract
Systemic and local vascular inflammation is implicated in atherogenesis. High-sensitivity (hs) assays detecting low concentrations of C-reactive protein (CRP) in healthy individuals have delineated associations of this inflammation marker with cardiovascular events years later. A 160 309-person participant level meta-analysis of 54 prospective studies found the relationship between log-CRP concentration and cardiovascular disease (CVD) events to be linear, with a 2.5-fold risk difference in individuals at opposite extremes of the CRP distribution.1In vitro studies and animal experiments point to potentially atherogenic actions of CRP,2 and statins lower both CRP and low-density lipoprotein cholesterol (LDL-C).3 The Justification for the Use of statins in Primary prevention: an Intervention Trial Evaluating Rosuvastatin (JUPITER),4 designed to ‘assess the effect of rosuvastatin on first ever cardiovascular events in apparently healthy men and women who do not qualify for statin therapy due to low levels of LDL-C, but who are at increased cardiovascular risk due to elevated levels of hs-CRP’, was halted because of ‘unequivocal evidence of a reduction in cardiovascular morbidity and mortality among patients who received CRESTOR (rosuvastatin) when compared to placebo’ (http://clinicaltrials.gov/ct2/show/NCT00239681). Consequently, the US Food and Drug Administration approved an amended license for rosuvastatin for the primary prevention of CVD events in men and women over 50 and 60 years, respectively, with one other risk factor and a CRP concentration of >2 mg/L.
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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 | 0.013 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".