Genetic Testing for Diagnosis of Hypertrophic Cardiomyopathy Mimics
Bibliographic record
Abstract
Background Genetic testing is helpful for diagnosis of hypertrophic cardiomyopathy (HCM) mimics. Little data are available regarding the yield of such testing and its clinical impact. Methods The HCM genetic database at our center was used for identification of patients who underwent HCM-directed genetic testing including at least 1 gene associated with an HCM mimic ( GLA , TTR , PRKAG2 , LAMP2 , PTPN11 , RAF1 , and DES ). Charts were retrospectively reviewed and genetic and clinical data extracted. Results There were 1731 unrelated HCM patients who underwent genetic testing for at least 1 gene related to an HCM mimic. In 1.45% of cases, a pathogenic or likely pathogenic variant in one of these genes was identified. This included a yield of 1% for Fabry disease, 0.3% for familial amyloidosis, 0.15% for PRKAG2 -related cardiomyopathy, and 1 patient with Noonan syndrome. In the majority of patients, diagnosis of the HCM mimic based on clinical findings alone would have been challenging. Accurate diagnosis of an HCM mimic led to change in management (eg, enzyme replacement therapy) or family screening in all cases. Conclusions Genetic testing is helpful in the diagnosis of HCM mimics in patients with no or few extracardiac manifestations. Adding these genes to all HCM genetic panels should be considered.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".