Bibliographic record
Abstract
C-reactive protein (CRP) plays a role in the pathogenesis of cardiovascular disease. It is a marker and predictor of cardiovascular disease. CRP possesses numerous cardiovascular effects (clotting, generation of oxygen radicals, increase in the expression of adhesion molecules and plasminogen activator inhibitor-1, plaque destabilization) that could result in cardiovascular disease. This review describes the effects of various cardiovascular drugs on the levels of CRP in health and disease. Cyclooxygenase inhibitors (aspirin, rofecoxib, celecoxib), platelet aggregation inhibitors (clopidogrel, abciximab), lipid lowering agents (statins, ezetimibe, fenofibrate, niacin, diets), beta-adrenoreceptor antagonists and antioxidants (vitamin E), as well as angiotensin converting enzyme (ACE) inhibitors (ramipril, captopril, fosinopril), reduce serum levels of CRP; while enalapril and trandolapril have not been shown to have the same effect. Angiotensin receptor blockers (ARBs) (valsartan, irbesartan, olmesartan, telmisartan) markedly reduce serum levels of CRP. The findings with other ARBs (losartan and candesartan) were inconsistent. Antidiabetic agents (rosiglitazone and pioglitazone) reduce CRP levels, while insulin is ineffective. Calcium channel antagonists have variable effects on CRP levels. Hydrochlorothiazide and oral estrogen do not affect CRP. The CRP-lowering effect of statins is more pronounced than their lipid lowering effect and is not dependent on their hypolipemic activity. The effect of atorvastatin on CRP seems to be dose-dependent. CRP-lowering effect of statins is likely to contribute to the favorable outcome of statin therapy. The data suggest that lipid lowering agents, ACE inhibitors, ARBs, antidiabetic agents, antiinflammatory and antiplatelet agents, vitamin E, and beta-adrenoreceptor antagonists lower serum or plasma levels of CRP, while vitamin C, oral estrogen and hydrochlorothiazide do not affect CRP levels.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.012 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.001 |
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 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".