Ethnic and racial differences in Asian populations with ion channelopathies associated with sudden cardiac death
Bibliographic record
Abstract
Cardiovascular diseases are associated with several morbidities and are the most common cause of worldwide disease-related fatalities. Studies show that treatment and outcome-related differences for cardiovascular diseases disproportionately affect minorities in the United States. The emergence of ethnic and racial differences in sudden cardiac death (SCD) and related ion channelopathies complicates cardiovascular disease prevention, diagnosis, management, prognosis, and treatment objectives for patients and physicians alike. This review compiles and synthesizes current research in cardiac ion channelopathies and genetic disorders in Asian populations, an underrepresented population in cardiovascular literature. We first present a brief introduction to SCD, noting relevant observations and statistics from around the world, including Asian populations. We then examined existing differences between Asian and White populations in research, treatment, and outcomes related to cardiac ion channelopathies and SCD, showing progression in thought and research over time for each ion channelopathy. The review also identifies research that explored phenotypic abnormalities, device usage, and risk of death in Asian patients. We touch upon the unique genetic risk factors in Asian populations that lead to cardiac ion channelopathies and SCD while comparing them to White and Western populations, particularly in the United States, where Asians comprise approximately 7% of the total population. We also propose potential solutions such as improving early genetic screening, addressing barriers affecting access to medical care and device utilization, physician training, and patient education on risks.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".