Epidemiology and Mechanisms of Uremia-Related Cardiovascular Disease
Bibliographic record
Abstract
Patients with chronic kidney disease and end-stage renal disease are at 5- to 10-fold higher risk for developing cardiovascular disease (CVD) than age-matched controls. Clinically, CVD in this population manifests as coronary artery disease, arrhythmias, stroke, or congestive heart failure. Beyond the traditional risk factors (eg, diabetes mellitus and hypertension), uremia-specific factors that arise from accumulating toxins also contribute to the pathogenesis of CVD. In this review, we summarize the literature on the epidemiology of both traditional and uremia-related CVD and focus on postulated mechanisms of the latter. In the context of current and emerging diagnostics and therapies for CVD, we highlight what we interpret as major gaps in the medical management of this growing population that need to be addressed with targeted epidemiological and translational research. Finally, we describe the global challenges associated with the recognition and management of uremia-related CVD in developed and developing nations.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".