Disparities in therapies for coronary artery disease with reduced left ventricular ejection fraction
Bibliographic record
Abstract
Revascularization through percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG) is used to manage left ventricular systolic dysfunction (LVSD) due to coronary artery disease (CAD). This review provides an overview of coronary revascularization for CAD with reduced left ventricular ejection fraction (LVEF), focusing on disparities in management. CABG provides more complete revascularization, and lower long-term all-cause mortality and reintervention and MI rates compared to PCI in patients with LVSD and CAD. Consequently, CABG is recommended as the primary revascularization therapy for CAD with reduced LVEF, with PCI being reserved for patients who are high-risk or have unfavorable coronary anatomy. Although LVSD increases revascularization risk, differential outcomes can be attributed to patients’ biological, behavioral, and socioeconomic factors as well as health system deficiencies. Women and racially and/or ethnically minoritized patients often present with progressive disease and greater comorbidity, experience delays in diagnosis and treatment, and have higher morbidity and mortality rates post-revascularization. These disparities may be explained by biological differences compounded by social determinants of health. Patients with CAD with LVSD pose unique medical challenges, which may be further complicated by disparities in care. Increased representation of minoritized patients in cardiovascular trials is needed to elucidate these differences and their long-term impact.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".