Underrepresentation of Renal Disease in Randomized Controlled Trials of Cardiovascular Disease
Why this work is in the frame
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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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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.020 | 0.014 |
| Bibliometrics | 0.001 | 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 it