Kidney Function Does Not Modify the Favorable Quality of Life Changes Associated With Revascularization for Coronary Artery Disease: Cohort Study
Bibliographic record
Abstract
BACKGROUND: Although patients with kidney disease have potential to benefit from revascularization, they are also at higher risk of complications, which may affect quality of life. METHODS AND RESULTS: We studied a cohort of 8198 adults who underwent coronary angiography in Alberta, between 2004 and 2008, and completed health-related quality-of-life (HR-QOL) surveys. Changes in HR-QOL measures were most favorable among patients who received coronary artery bypass graft (CABG), but did not significantly differ by kidney function within groups of patients who received CABG, percutaneous coronary intervention (PCI), or medical therapy (P value for interaction between estimated glomerular filtration rate [eGFR] and revascularization status >0.10 for all outcomes). Among those who received CABG, the adjusted mean EuroQol 5 dimensions (EQ-5D) utility score for those with eGFR >90 mL/min per 1.73 m(2) increased by 0.11 (95% CI, 0.09-0.14) and for those with eGFR <30 mL/min per 1.73m(2) by 0.13 (95% CI, 0.05-0.21). The adjusted mean EQ-5D utility score also increased similarly at all levels of eGFR for those who received PCI and for those who received medical management. Mean changes in Seattle Angina Questionnaire (SAQ) scores were also similar across all levels of eGFR within each treatment group for the quality of life, angina frequency, angina stability, physical limitations, and treatment satisfaction domains of the SAQ. Among those who received CABG, the adjusted mean SAQ quality of life score for those with eGFR >90 mL/min per 1.73m(2) increased by 22.1 (95% CI, 18.5-25.7) and for those with eGFR <30 mL/min per 1.73m(2) by 14.0 (95% CI, 2.31-25.63). CONCLUSIONS: Changes in HR-QOL do not vary by kidney function among patients selected for CABG, PCI, or medical management of coronary disease.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".