Revascularization strategies for multivessel coronary artery disease based on sex and age
Bibliographic record
Abstract
OBJECTIVES: This study describes coronary revascularization strategies used by sex and age in the USA. METHODS: A sex-stratified cohort study from the National Inpatient Sample from the Agency for Healthcare Research and Quality (USA) including patients admitted for coronary revascularization with primary or secondary diagnoses of chronic coronary syndrome or non-ST elevation myocardial infarction who underwent ≥3-vessel coronary artery bypass grafting or percutaneous coronary intervention from January 2019 to December 2020. The primary outcome was the use rate of coronary artery bypass grafting or multivessel percutaneous coronary intervention. Prespecified subgroups included age and non-ST elevation myocardial infarction. RESULTS: Among 121 150 patients (21.7% women), there were no sex differences in age (women: 66.6 [66.5-66.7], men: 67.6 [67.5-67.7], standardized mean difference: 0.1) or non-ST elevation myocardial infarction incidence (women: 37.4%, men: 45.7%, standardized mean difference: 0.17). The majority of women (74.2%) and men (84.9%) underwent bypass grafting, which was unaffected by age, race or non-ST elevation myocardial infarction. Women were less likely to undergo bypass grafting than percutaneous intervention (adjusted odds ratio 0.49, 95% confidence interval 0.44-0.54; P < 0.001) and a disparity most pronounced in patients >80 years old (adjusted odds ratio 0.31, 95% confidence interval 0.22-0.45; P < 0.001). CONCLUSIONS: Most patients with multivessel coronary artery disease needing revascularization undergo bypass grafting, irrespective of sex, age or clinical presentation. The sex disparity in the use of bypass grafting is mostly seen among patients >80 years old.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".