Septal myectomy in the era of genetic testing
Bibliographic record
Abstract
BACKGROUND: Hypertrophic obstructive cardiomyopathy (HOCM) is one of the most common genetic cardiac diseases and encompasses an array of clinical presentations. Little is known about the impact of genetic background on outcomes after septal myectomy (SM). The aim of this study was to evaluate the effect of specific genetic mutations on midterm outcomes in adults undergoing SM for HOCM. METHODS: From 2003 to 2020, a total of 59 patients (male = 66%, mean age = 52 ± 13) underwent SM after a preoperative genetic test. Patients were divided into two groups according to their test result (positive or negative). Preoperative echocardiograms were examined to identify phenotypical characteristics of each mutation. RESULTS: A total of thirty-one patients (53%) had a positive genetic test. MYBPC3 was the most common mutation (15/31 patients). Four different phenotypes were identified on preoperative echocardiograms. Overall, Type 1 phenotype was the most common (37% of the cohort). Type 3 was found exclusively in patients with a positive genetic test. Following SM, none of the patients required a redo myectomy or septal ablation. At 10 years, the survival was 97 ± 3% and 100% in patients with a positive and negative genetic test (p = .33), respectively. CONCLUSION: Although our results suggest that the multiple gene mutations present with different characteristics and phenotypes, midterm results of SM appear to be good regardless of genetic mutation presence.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".