Abstract 17186: Regional Extracellular Volume Fraction Can Identify Cardiac Muscle Changes in Patients With Duchenne Muscular Dystrophy
Bibliographic record
Abstract
Background: Duchenne muscular dystrophy (DMD) is an X-linked inherited disorder causing dilated cardiomyopathy with variable onset and progression. Our aim was to compare native T1 mapping and extracellular volume fraction (ECV) in boys with DMD to controls to identify markers of preclinical myocardial disease. Methods: With IRB approval and informed consent/assent, 17 boys with DMD had cardiac magnetic resonance imaging (MRI) with contrast. Ejection fraction (EF), presence of late gadolinium enhancement (LGE), native T1, and ECV mapping were obtained using a modified Look-Locker sequence on a 1.5T MR scanner (Figure 1). Results were compared with age and gender matched controls with no history predisposing to cardiac fibrosis (i.e. bypass, myocarditis, etc). Results: Seventeen DMD subjects (age 14 ± 5 years; EF 56% ± 8%) were compared with 13 normal controls (age 17 ± 4 years) with normal EF and no LGE. DMD subjects had low-normal EF (average EF 56% ± 8%) and 10 of 17 (59%) had evidence of LGE, all in the lateral location. Comparing native T1 in DMD vs controls, septal native T1 was 1060 ± 71 ms versus 990 ± 34 ms (p < 0.01) and lateral native T1 was 1090 ± 93 versus 978 ± 37 ms, respectively (p < 0.01). Comparing ECV values in DMD vs controls, septal ECV was 29.6 ± 7.8 and 25.9 ± 3.4 (p = 0.03) and lateral ECV was 32.7 ± 8.7 and 24.4 ±3.6 (p < 0.01), respectively. All DMD patients with abnormal EF and/or presence of LGE also had higher ECV values. Conclusions: ECV is strongly-related to DMD and differs significantly compared to normals, particularly in the lateral wall. ECV mapping may be a useful technique for identifying preclinical myocardial changes in DMD. Figure 1. A) Precontrast T1 map, B) post contrast T1 map and C) ECV map with scale on the right in the short axis projection of a boy with Duchenne muscular dystrophy. Arrows indicate elevated values laterally.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".