Revascularization vs. Medical Therapy for Coronary Chronic Total Occlusions in Patients With Chronic Kidney Disease
Bibliographic record
Abstract
BACKGROUND: We investigated whether the outcome of revascularization differed from the outcome of medical therapy in chronic kidney disease (CKD) and non-CKD patients with chronic total occlusion (CTO). METHODS AND RESULTS: A total of 2,010 patients with CTO who underwent revascularization (n=1,355), including percutaneous coronary intervention (n=878) and coronary artery bypass grafting (n=477), or had medical therapy alone (n=655) were examined. The primary outcome was all-cause death during follow-up. Among the non-CKD patients (n=1,679), revascularization had a lower incidence of all-cause death (adjusted hazard ratio [HR] 0.54, 95% confidence interval [CI] 0.41-0.72, P<0.001) compared with medical therapy. Among the CKD patients (n=331), the difference in the incidence of all-cause death was not as marked between the 2 treatments (adjusted HR 0.71, 95% CI 0.48-1.06, P=0.09). There was a significant interaction between kidney function and treatment strategy (revascularization vs. medical therapy) on all-cause death (P for interaction=0.014). CONCLUSIONS: Based on the clinical outcomes, in CTO patients with preexisting CKD, revascularization via PCI or bypass surgery might not be as effective as in non-CKD patients.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".