Quantitative magnetic resonance imaging measures as biomarkers of disease progression in boys with Duchenne muscular dystrophy: a phase 2 trial of domagrozumab
Bibliographic record
Abstract
Duchenne muscular dystrophy (DMD) is a progressive, neuromuscular disorder caused by mutations in the DMD gene that results in a lack of functional dystrophin protein. Herein, we report the use of quantitative magnetic resonance imaging (MRI) measures as biomarkers in the context of a multicenter phase 2, randomized, placebo-controlled clinical trial evaluating the myostatin inhibitor domagrozumab in ambulatory boys with DMD (n = 120 aged 6 to < 16 years). MRI scans of the thigh to measure muscle volume, muscle volume index (MVI), fat fraction, and T2 relaxation time were obtained at baseline and at weeks 17, 33, 49, and 97 as per protocol. These quantitative MRI measurements appeared to be sensitive and objective biomarkers for evaluating disease progression, with significant changes observed in muscle volume, MVI, and T2 mapping measures over time. To further explore the utility of quantitative MRI measures as biomarkers to inform longer term functional changes in this cohort, a regression analysis was performed and demonstrated that muscle volume, MVI, T2 mapping measures, and fat fraction assessment were significantly correlated with longer term changes in four-stair climb times and North Star Ambulatory Assessment functional scores. Finally, less favorable baseline measures of MVI, fat fraction of the muscle bundle, and fat fraction of lean muscle were significant risk factors for loss of ambulation over a 2-year monitoring period. These analyses suggest that MRI can be a valuable tool for use in clinical trials and may help inform future functional changes in DMD.Trial registration: ClinicalTrials.gov identifier, NCT02310763; registered December 2014.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".