Optimal Method of Coronary Revascularization in Patients Receiving Dialysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Patients receiving dialysis have a high burden of cardiovascular disease. Some receive coronary artery revascularization but the optimal method is controversial. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: The authors reviewed any randomized controlled trial or cohort study of 10 or more patients receiving maintenance dialysis which compared coronary artery bypass graft (CABG) to percutaneous intervention (PCI) for revascularization of the coronary arteries. The primary outcomes were short-term (30 d or in-hospital) and long-term (at least 1 year) mortality. RESULTS: Seventeen studies were found. There were no randomized trials: all were retrospective cohort studies from years 1977 to 2002. There were some baseline differences between the groups receiving CABG compared with those receiving PCI, and most studies did not consider results adjusted for such characteristics. Given the variability among studies and their methodological limitations, few definitive conclusions about the optimal method of revascularization could be drawn. In an exploratory meta-analysis, short-term mortality was higher after CABG compared to PCI. A substantial number of patients died over a subsequent 1 to 5 yr, with no difference in mortality after CABG compared to PCI. CONCLUSIONS: Although decisions about the optimal method of coronary artery revascularization in dialysis patients are undertaken routinely, it was surprising to see how few data has been published in this regard. Additional research will help inform physician and patient decisions about coronary artery revascularization.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".