Tissue Doppler Imaging Consistently Detects Myocardial Abnormalities in Patients With Hypertrophic Cardiomyopathy and Provides a Novel Means for an Early Diagnosis Before and Independently of Hypertrophy
Bibliographic record
Abstract
BACKGROUND: Left ventricular hypertrophy (LVH), the clinical hallmark of familial hypertrophic cardiomyopathy (FHCM), is absent in a significant number of subjects with causal mutations. In transgenic rabbits that fully recapitulate the FHCM phenotype, reduced myocardial tissue Doppler (TD) velocities accurately identified the mutant rabbits, even in the absence of LVH. We tested whether humans with FHCM also consistently showed reduced myocardial TD velocities, irrespective of LVH. METHODS AND RESULTS: We performed 2D and Doppler echocardiography and TD imaging in 30 subjects with FHCM, 13 subjects who were positive for various mutations but did not have LVH, and 30 age- and sex-matched controls (all adults; 77% women). LV wall thickness and mass were significantly greater in FHCM subjects (P<0.01 versus those without LVH and controls). There were no significant differences in 2D echocardiographic, mitral, and pulmonary venous flow indices between mutation-positives without LVH and controls. In contrast, systolic and early diastolic TD velocities were significantly lower in both mutation-positives without LVH and in FHCM patients than in controls (P<0.001). Reduced TD velocities had a sensitivity of 100% and a specificity of 93% for identifying mutation-positives without LVH. CONCLUSIONS: Myocardial contraction and relaxation velocities, detected by TD imaging, are reduced in FHCM, including in those without LVH. Before and independently of LVH, TD imaging is an accurate and sensitive method for identifying subjects who are positive for FHCM mutations.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".