Single Versus Multiple Arterial Revascularization in Patients With Reduced Renal Function
Bibliographic record
Abstract
Objective: To compare the long-term outcomes of MAR versus SAR in patients with renal insufficiency. Summary of Background Data: Previous studies have been insufficiently powered to address whether MAR confers long-term benefit over SAR in patients with renal dysfunction who require CABG. Methods: We conducted retrospective cohort study in Ontario, Canada of patients who underwent isolated CABG (n = 23,406). The primary outcome was MACE, defined as the composite of stroke, myocardial infarction, and repeat revascularization. We compared patients by matching them on the propensity to have received SAR versus MAR, within groups with preoperative glomerular filtration rate (GFR) ≥60 mL/min/1.73 m2; GFR between 30 and 60; and GFR <30. Results: In patients with GFR ≥60, the use of MAR versus SAR was associated with a lower rate of MACE [hazard ratio (HR) 0.87 (0.80–0.94)], and a lower rate of long-term mortality [HR 0.87 (0.79–0.97)]. In those with GFR between 30 and 60, MAR was not associated with a difference in MACE [HR 1.04 (0.87–1.26)], and a lower rate of long-term mortality [HR 0.75 (0.65–0.87)] was observed. In those with GFR <30, MAR was not associated with a difference in outcomes. Conclusions: MAR versus SAR does not correlate with a difference in MACE amongst patients with GFR between 30 and 60 and better survival raises the possibility of indication bias. Furthermore, MAR did not confer a benefit in those with severely reduced renal function. These data suggest that the potential long-term benefits of using MAR in CABG patients with renal insufficiency may be offset by competing health risks.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".