THE CLINICAL EPIDEMIOLOGY OF CARDIOVASCULAR DISEASES IN CHRONIC KIDNEY DISEASE: Uremia‐Related Metabolic Cardiac Risk Factors in Chronic Kidney Disease
Bibliographic record
Abstract
Growing evidence has been gathered over the last 15 years regarding the role of nontraditional or uremia-related risk factors in the pathogenesis of atherosclerosis in subjects with renal failure. Among those factors, dyslipidemia, inflammation, hyperhomocysteinemia, and oxidant stress have been extensively studied. However, the clinical significance of many of these factors remains controversial in light of reported studies. In this article, the existing evidence regarding the role of uremia-related risk factors in the pathogenesis of atherosclerosis is reviewed, with special emphasis on prevalence, cardiac risk, and management in patients with chronic kidney disease (CKD). Consensus treatment recommendations are provided for risk factors for which there is evidence to support preventive or therapeutic interventions.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.013 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".