Abstract 2299: Post-CABG Patients with Non-Revascularizable Coronary Artery Disease - A Population Based Study from Olmsted County, Minnesota
Bibliographic record
Abstract
Advances in molecular and cellular biology have supported the development of new therapeutic approaches to limiting ischemia. Most often these approaches target patients with ischemia post coronary artery bypass grafting (CABG). Currently, there are few data available on the incidence and outcomes of such patients. To understand this important patient cohort and to estimate the potential need for new therapies, we performed a population-based study of post CABG patients who underwent coronary and graft angiography for angina from 2001 – 2005. Patients who underwent coronary artery and bypass graft angiography at Mayo Clinic from 2001 – 2005 were identified. Medical records were reviewed to determine indication for angiography, and angiographic analysis was performed in all patients. The study cohort was then followed prospectively until March 1 2008. Among 133,000 residents of Olmsted County, Minnesota, 347 post bypass patients with angina underwent coronary angiography from 2001 – 2005. Of those, 51% (177 patients) received further revascularization (endovascular and surgical) and the remaining 170 patients were managed medically. There was no statistical difference in terms of all cause mortality and cardiac mortality between the group that received further revascularization and the group that did not. Adjusting for age and sex for differences between the Olmsted County population and the US white 2000 population yields a yearly incidence rate of 17.9 (95% CI 14.2–21.6) to 33.2 (95% CI 28.2 – 38.3) patients with non-revascularizable angina following CABG per 100,000 population. There was no difference in all cause or cardiac mortality in post-CABG patients presenting with angina whether they received further endovascular/surgical revascularization or not. We estimate that non-revascularizable coronary artery disease post CABG arises in approximately 50,000 (95% CI 40,000 – 61,000) to 93,000 (95% CI 79,000 – 110,000) patients per year extrapolating to the entire US population as a whole.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".