Underrepresentation of Renal Disease in Randomized Controlled Trials of Cardiovascular Disease
Bibliographic record
Abstract
CONTEXT: Patients with renal disease are at high risk for cardiovascular mortality. Determining which interventions best offset this risk remains a health priority. OBJECTIVE: To quantify the representation of patients with renal disease in randomized controlled trials for interventions proven efficacious for cardiovascular disease. DATA SOURCES: We searched MEDLINE for trials published from 1985 through 2005 in 11 major medical and subspecialty journals. STUDY SELECTION: Randomized controlled trials for chronic congestive heart failure and acute myocardial infarction of treatments that are currently listed as class I or II recommendations in the current American College of Cardiology/American Heart Association guidelines were included. DATA EXTRACTION: Two reviewers independently abstracted data on study and patient characteristics, renal measurements, outcomes, and prognostic features. DATA SYNTHESIS: A total of 153 trials were reviewed. Patients with renal disease were reported as excluded in 86 (56%) trials. Patients with renal disease were more likely to be excluded from trials that were multicenter; of moderate enrollment size; North American; that tested renin-angiotensin-aldosterone system antagonists and anticoagulants; and that tested chronic congestive heart failure. Only 8 (5%) original articles reported the proportion of enrolled patients with renal disease, and only 15 (10%) reported mean baseline renal function. While 81 (53%) trials performed subgroup analyses of some baseline characteristic in the original article, only 4 (3%) subgroup analyses of treatment stratified by renal disease were performed. CONCLUSION: Major cardiovascular disease trials frequently exclude patients with renal disease and do not provide adequate information on the renal function of enrollees or the effect of interventions on patients with renal disease.
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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 | 0.613 | 0.824 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.014 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier 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".