Cardiovascular disease in chronic kidney disease
Bibliographic record
Abstract
Individuals with chronic kidney disease (CKD) exhibit an increased risk for the development of cardiovascular disease (CVD) with its manifestations coronary artery disease, stroke, heart failure, arrhythmias, and sudden cardiac death. The presence of both, CVD and CKD has a major impact on the prognosis of patients. This association likely reflects the involvement of several pathophysiological mechanisms, including shared risk factors (e.g. diabetes and hypertension), as well as other factors such as inflammation, anaemia, volume overload, and the presence of uraemic toxins. Identifying and characterizing CKD is crucial for appropriate CVD risk prediction. Mitigating CVD risk in patients with CKD mandates a multidisciplinary approach involving cardiologists, nephrologists, and other health care professionals. The present State-of-the-Art Review addresses the current understanding on the pathophysiological link between CVD and CKD, clinical implications and challenges in the treatment of these patients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".