Exploring Health Care Disparities in Genetic Testing and Research for Hereditary Cardiomyopathy: Current State and Future Perspectives
Bibliographic record
Abstract
Background Hereditary cardiomyopathies are commonly occurring myocardial conditions affecting heart structure and function with a genetic or familial association, but the etiology is often unknown. Cardiomyopathies are linked to significant mortality, requiring robust risk stratification with genetic testing and early diagnosis. Hypothesis We hypothesized that health care disparities exist in genetic testing for hereditary cardiomyopathies within clinical practice and research studies. Methods In a narrative fashion, we conducted a literature search with online databases such as PubMed/MEDLINE, Google Scholar, EMBASE, and Science Direct on papers related to hereditary cardiomyopathies. A comprehensive analysis of findings from articles in English on disparities in diagnostics and treatment was grouped into four categories. Results Racial and ethnic disparities in research study enrollment and health care delivery favor White populations and higher socioeconomic status, resulting in differences in the development and implementation of effective genetic screening. Such disparities have shown to be detrimental, as minorities often suffer from disease progression to heart failure and sudden cardiac death. Barriers related to clinical genetic testing included insurance-related issues and health illiteracy. The underrepresentation of minority populations extends to research methodologies, as testing in ethnic minorities resulted in a significantly lower detection rate and diagnostic yield, as well as a higher likelihood of misclassification of variants. Conclusions Prioritizing minority-based participatory research programs and screening protocols can address systemic disparities. Diversifying research studies can improve risk stratification strategies and impact clinical practice.
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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 | 0.098 | 0.148 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".