Arrhythmias and Ion Channelopathies Causing Sudden Cardiac Death in Hispanic/Latino and Indigenous Populations
Bibliographic record
Abstract
The limited literature and increasing interest in studies on cardiac electrophysiology, explicitly focusing on cardiac ion channelopathies and sudden cardiac death (SCD) in diverse populations, has prompted a comprehensive examination of existing research. Our review specifically targets Hispanic/Latino and Indigenous populations, which are often underrepresented in healthcare studies. This review encompasses investigations into genetic variants, epidemiology, etiologies, and clinical risk factors associated with arrhythmias in these demographic groups. The review explores the Hispanic paradox, a phenomenon linking healthcare outcomes to socioeconomic factors within Hispanic communities in the United States. Furthermore, it discusses studies exemplifying this observation in the context of arrhythmias and ion channelopathies in Hispanic populations. Current research also sheds light on disparities in overall healthcare quality in Indigenous populations. The available yet limited literature underscores the pressing need for more extensive and comprehensive research on cardiac ion channelopathies in Hispanic/Latino and Indigenous populations. Specifically, additional studies are essential to fully characterize pathogenic genetic variants, identify population-specific risk factors, and address health disparities to enhance the detection, prevention, and management of arrhythmias and SCD in these demographic groups.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".