Cardiovascular outcome trials in patients with chronic kidney disease: challenges associated with selection of patients and endpoints
Bibliographic record
Abstract
Although cardiovascular disease is a major health burden for patients with chronic kidney disease, most cardiovascular outcome trials have excluded patients with advanced chronic kidney disease. Moreover, the major cardiovascular outcome trials that have been conducted in patients with end-stage renal disease have not demonstrated a treatment benefit. Thus, clinicians have limited evidence to guide the management of cardiovascular disease in patients with chronic kidney disease, particularly those on dialysis. Several factors contribute to both the paucity of trials and the apparent lack of observed treatment effect in completed studies. Challenges associated with conducting trials in this population include patient heterogeneity, complexity of renal pathophysiology and its interaction with cardiovascular disease, and competing risks for death. The Investigator Network Initiative Cardiovascular and Renal Clinical Trialists (INI-CRCT), an international organization of academic cardiovascular and renal clinical trialists, held a meeting of regulators and experts in nephrology, cardiology, and clinical trial methodology. The group identified several research priorities, summarized in this paper, that should be pursued to advance the field towards achieving improved cardiovascular outcomes for these patients. Cardiovascular and renal clinical trialists must partner to address the uncertainties in the field through collaborative research and design clinical trials that reflect the specific needs of the chronic and end-stage kidney disease populations, with the shared goal of generating robust evidence to guide the management of cardiovascular disease in patients with kidney disease.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; 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".