Creating controversy where none exists: the important role of C-reactive protein in the CARE, AFCAPS/TexCAPS, PROVE IT, REVERSAL, A to Z, JUPITER, HEART PROTECTION, and ASCOT trials
Bibliographic record
Abstract
This editorial refers to ‘Evaluation of C-reactive protein prior to and on-treatment as a predictor of benefit from atorvastatin: observations from the Anglo-Scandinavian Cardiac Outcomes Trial’[†][1], by P.S. Sever et al. , on page 486 Based on substantial laboratory evidence that inflammation plays a major role in atherothrombosis, clinical investigators showed in the mid-1990s that inflammatory biomarkers, such as C-reactive protein, serum amyloid A, and fibrinogen were associated with increased risk of first as well as recurrent cardiovascular events.1,2 It has since become clear from a meta-analysis of >50 prospective cohort studies involving >160 000 subjects that the magnitude of risk associated with C-reactive protein (and other inflammatory markers) is as large as that of total cholesterol and of blood pressure.3 The addition of C-reactive protein and family history to the usual Framingham risk factors, as in the Reynolds Risk Score, has been shown to improve significantly the ability to estimate 10-year cardiovascular risk.4 The ability of C-reactive protein to assess risk, especially in the reclassification of persons at intermediate risk, has been demonstrated in a number of populations, including those from the MONICA5 and Framingham6 studies. Given the consistency of these data and the pathophysiological importance of inflammation in vascular disease, it is not surprising that C-reactive protein evaluation has become a part of prevention guidelines in the USA and Canada, particularly for those at intermediate risk. Beyond use as an effective tool for the prediction of vascular risk, the level of C-reactive protein measured in patients receiving statins also has clinical relevance as a method to define patients who will benefit from this therapy, … [1]: #fn-2
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 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 | 0.012 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.004 |
| 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".