The evolution of gene‐guided management of inherited arrhythmia syndromes: Peering beyond monogenic paradigms towards comprehensive genomic risk scores
Bibliographic record
Abstract
Inherited arrhythmia syndromes have traditionally been viewed as monogenic forms of disease whose pathophysiology is driven by a single highly penetrant rare genetic variant. Although an accurate depiction of a proportion of genetic variants, the variable penetrance frequently noted in genotype positive families and the presence of sporadic genotype negative cases have long highlighted a more nuanced truth being operative. Coupled with our more recent recognition that many rare variants implicated in inherited arrhythmia syndromes possess unexpectedly high allele frequencies within the general population, these observations have contributed to the realization that a spectrum of pathogenicity exists among clinically relevant genetic variants. Notably, variable mutation pathogenicity and corresponding variable degrees of penetrance emphasize a limitation of contemporary guidelines, which attempt to dichotomize genetic variants as pathogenic or benign. Recognition of the existence of low and intermediate penetrant variants insufficient to be causative for disease in isolation has served to emphasize the importance of additional genetic, clinical, and environmental factors in the pathogenesis of rare inherited arrhythmia syndromes. Despite being rare, it has also become increasingly evident that common genetic variants play critical roles in both heritable channelopathies and cardiomyopathies and in aggregate may even be the primary drivers in certain instances, such as genotype negative Brugada syndrome. Our growing realization that the genetic substrates of inherited arrhythmia syndromes have intricacies that extend beyond traditionally perceived monogenic paradigms has highlighted a potential value of leveraging more comprehensive genomic risk scores for predicting disease development and arrhythmic risk.
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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 | 0.053 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.011 |
| 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".