Racial Disparities in Ion Channelopathies and Inherited Cardiovascular Diseases Associated With Sudden Cardiac Death
Bibliographic record
Abstract
Cardiovascular disease (CVD) continues to be the most common cause of death worldwide, and cardiac arrhythmias account for approximately one half of these deaths. The morbidity and mortality from CVD have been reduced significantly over the past few decades; however, disparities in racial or ethnic populations still exist. This review is based on available literature to date and focuses on known cardiac channelopathies and other inherited disorders associated with sudden cardiac death in African American/Black subjects and the role of epigenetics in phenotypic manifestations of CVD, and illustrates existing disparities in treatment and outcomes. The review also highlights the knowledge gaps that limit understanding of the manifestation of phenotypic abnormalities across racial or ethnic groups and discusses disparities associated with device underuse in the management of patients at risk for sudden cardiac death. We discuss factors related to reports in the United States, that the overall mortality attributed to CVD and the number of out-of-hospital cardiac arrests are higher among African American/Black subjects when compared with other racial or ethnic groups. African American/Black subjects are disproportionally affected by CVD, including cardiac arrhythmias and sudden cardiac death, thus highlighting a major concern in this population that remains underrepresented in clinical trials with limited genetic testing and device underuse. The proposed solutions include (1) early identification of genetic variants, which is crucial in tailoring a preventive management strategy; (2) inclusion of diverse racial or ethnic groups in clinical trials; (3) compliance with guideline-directed medical treatment and referral to cardiovascular subspecialists; and (4) training and mentoring of underrepresented junior faculty in cardiovascular health disparities research.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".