Cardiac MRI in Duchenne and Becker Muscular Dystrophy
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Cardiovascular magnetic resonance imaging (CMRI) is the noninvasive technique of choice for early detection of cardiac involvement in Duchenne and Becker muscular dystrophy (DMD and BMD, respectively), but is seldom used in routine clinical practice in the Indian context. We sought to determine the prevalence of CMRI abnormalities in patients with DMD and BMD and to compare the CMRI parameters with the phenotypic and genotypic characteristics. METHODS: A prospective, observational study was conducted on patients genetically diagnosed with DMD and BMD who could complete CMRI between March 2020 and March 2022. Abnormal CMRI was the presence of any late gadolinium enhancement (LGE) that signifies myocardial fibrosis (LGE positivity), regional wall motion abnormality, or reduced left ventricular ejection fraction (LVEF <55%). RESULTS: A total of 46 patients were included: 38 patients with DMD and eight with BMD. Cardiac abnormality was seen in 23 (50%) patients. LGE was more common than impaired LVEF in DMD (16, 42.1%), while impaired LVEF was more common in BMD (5, 62.5%). LGE was most frequently found in lateral wall (18/19) followed by inferior (6/19), septal (5/19), anterior (2/19), and apex (1/19). Among the various clinicodemographic parameters, only age ( r = 0.495, P = 0.002) and disease duration ( r = 0.407, P = 0.011) were found to significantly correlate with LGE in patients with DMD. No association was found between the various CMRI parameters and the genotype. CONCLUSIONS: The current study highlights the differences in myocardial fibrosis and LV dysfunction between DMD and BMD, along with other CMRI parameters. Notably, a genotype-CMRI correlation was not found in the current cohort, which needs to be further explored.
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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 | 0.001 |
| 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 | 0.001 | 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".