Racial and ethnic differences in aortic stenosis: the tip of the iceberg
Bibliographic record
Abstract
PURPOSE OF REVIEW: There is a lack of data on the epidemiology and management of severe aortic stenosis (AS) in diverse populations. We summarize the contemporary literature on the racial and ethnic differences in AS prevalence, treatment and outcomes and discuss possible explanations for these disparities to inform future research and improve the delivery of care to under-represented patient groups. RECENT FINDINGS: African American (AA) patients have significantly less prevalence of severe AS than White patients whereas paradoxically having higher traditional risk factors for severe AS. Non-White patients have less referral for aortic valve replacement (AVR) after adjusting for clinical and echocardiographic parameters. Surgical aortic valve replacement (SAVR) and transcatheter aortic valve replacement (TAVR) are both underutilized in non-White patients. Differences in race and ethnicity have not shown to result in worse in-hospital and long-term survival outcomes after either SAVR or TAVR. SUMMARY: Much research is warranted to explore the epidemiology, true prevalence and treatment outcomes of severe AS in diverse populations. Greater inclusion of non-White ethnic groups in the primary analysis of prospective trials is needed. Lastly, further research is warranted to explore the complex causes of racial and ethnic disparities in utilization of surgical and transcatheter interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".