Long-term patient and kidney survival after coronary artery bypass grafting, percutaneous coronary intervention, or medical therapy for patients with chronic kidney disease
Bibliographic record
Abstract
BACKGROUND: Revascularization in patients with chronic kidney disease (CKD) and coronary artery disease (CAD) is often deferred because of concern over progression of renal failure. HYPOTHESIS: Revascularization with either coronary artery bypass grafting (CABG) or percutaneous coronary intervention (PCI) leads to progression of renal failure, but improves survival compared with medical therapy in patients with CKD. PATIENTS AND METHODS: Linkages between the British Columbia Cardiac Registry and the British Columbia Renal Registry of patients with established CAD and CKD who underwent CABG, PCI, or were treated medically were propensity matched. Overall patient survival was analyzed using a Cox proportional hazard model. Primary renal outcomes, defined as patients requiring long-term dialysis or progressive loss in kidney function, were analyzed using a competing risk approach. RESULTS: On the basis of the matched cohort, the risk of renal outcome in the first three months was the highest in the CABG group, but comparable between the PCI and the medical group (estimated probability at 3 months: 12.7% for CABG, 5.4% for PCI, 4.4% for medical; P<0.01). The estimated probability for the renal outcome at 24 months was similar across the groups: 37.9% for CABG, 37.6% for PCI, and 35.2% for medical therapy (P=0.62). The mortality risk at 24 months was lower for CABG (3.9%) compared with PCI (14.5%) or medical therapy (16.4%) (P<0.01). CONCLUSION: In patients with CAD and CKD who undergo the current practice of CABG, PCI, or are treated with medical therapy, progression of renal failure is higher in the first 3 months for CABG, but similar for all groups at 24 months. The 2-year mortality is lower in patients treated with CABG compared with PCI or medical therapy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".