Clinical prediction of genotypes in hypertrophic cardiomyopathy: A systematic review
Bibliographic record
Abstract
INTRODUCTION: Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiac condition and the most common cause of sudden cardiac death (SCD) in patients below the age of 35. Genetic testing is a vital part of HCM diagnostics, yet correlation with clinical phenotypes remains complex. Identifying clinical predictors of informative genetic testing may prevent unnecessary investigations and improve cost-effectiveness of services. This article reviews the current literature pertinent to identifying such predictors. METHODS: Five literature databases were screened using a suitably designed search strategy. Studies investigating the correlation between having a positive genetic test for HCM and a range of clinical and radiological parameters were included in the systematic review. RESULTS: Twenty-nine observational studies of a total of 9,486 patients were included. The main predictors of informative genetic testing were younger age, higher septal thickness, reverse septal curvature, family history of HCM and SCD and the absence of hypertension. Two externally validated scoring systems have also been developed: the Mayo and Toronto scores. Novel imaging markers and complex algorithmic models are emerging predictors. CONCLUSION: Using clinical predictors to decide whom to test is a feasible alternative to investigating all comers. Nonetheless, currently there is not enough evidence to unequivocally recommend for or against this strategy. Further validation of current predictors and identification of new ones remain open research avenues.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.004 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".