The clinical epidemiology of cardiovascular disease in chronic kidney disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: To review recent publications concerning the epidemiology and management of cardiovascular disease in the stages of chronic kidney disease. RECENT FINDINGS: Chronic kidney disease is a state of increased risk for atherosclerotic and cardiomyopathic disease. The mechanisms of cardiovascular disease probably change with the different stages of chronic kidney disease. Both proteinuria and decreased glomerular filtration rate are probably independent cardiovascular disease risk factors, although the impact of the latter is modest. Traditional risk factors are important predictors of cardiovascular disease in chronic kidney disease. Recent randomized controlled trials and cohort studies have supported interventions for smoking cessation, blood pressure control, renin-angiotensin system blockade, the correction of lipid abnormalities, and utilizing antiplatelet agents. Some uraemia-related risk factors predict the development of cardiovascular disease, particularly hypoalbuminaemia, inflammation, anaemia, and homocysteinaemia. However, randomized controlled trials of anaemia correction and of an increased quantity of dialysis were negative. SUMMARY: The role of oxidant stress, divalent ion abnormalities, various lipid abnormalities and other potential factors require further investigation. To determine whether these uraemia-related factors are markers of cardiovascular disease risk or are actually cardiotoxic requires additional randomized controlled trials.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".