Late Gadolinium Enhancement for Prediction of Mutation-Positive Hypertrophic Cardiomyopathy on the Basis of Panel-Wide Sequencing
Bibliographic record
Abstract
BACKGROUND: Cardiac magnetic resonance (CMR) with late gadolinium enhancement (LGE) revealed a substantial variation in the extent of myocardial scarring, a pathological hallmark of hypertrophic cardiomyopathy (HCM). However, few data exist regarding the relationship between the presence of gene mutations and the extent of LGE. Therefore, we aimed to investigate whether variations in the extent of LGE in HCM patients can be explained by the presence or absence of disease-causing mutations. METHODS AND RESULTS: We analyzed data from 82 unrelated HCM patients who underwent both LGE-CMR and next-generation sequencing. We identified disease-causing sarcomere gene mutations in 44 cases (54%). The extent of LGE on CMR was an independent factor for predicting mutation-positive HCM (odds ratio 2.12 [95% confidence interval 1.51-3.83], P<0.01). The area under the curve of %LGE was greater than that of the conventional Toronto score for predicting the presence of a mutation (0.96 vs. 0.69, P<0.01). Sensitivity, specificity, positive predictive value, and negative predictive value of %LGE (cutoff >8.1%) were 93.2%, 89.5%, 91.1%, and 91.9%, respectively. CONCLUSIONS: The results demonstrated that %LGE clearly discriminated mutation-positive from mutation-negative HCM in a clinically affected HCM population. HCM with few or no myocardial scars may be genetically different from HCM with a higher incidence of myocardial scars.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".