Modeling Early Heterogeneous Rates of Progression in Boys with Duchenne Muscular Dystrophy
Bibliographic record
Abstract
BACKGROUND: Duchenne muscular dystrophy (DMD) exhibits substantial variability in rates of disease progression and response to treatment. This has hindered treatment development and complicated interpretation of drug effects in clinical trials. OBJECTIVE: We hypothesized that a multivariate combination of early-age clinical outcome measurements can explain differential disease progression. METHODS: Data on boys with DMD (ages 4-<10 years), both treated with steroidal anti-inflammatories and untreated, were obtained from CINRG Duchenne Natural History Study (n = 209) and vamorolone VBP15-002/003/LTE (n = 46) studies. Velocities from three timed function tests (TFTs; stand from supine, run/walk 10 meters, and climb 4 stairs) were simultaneously modeled in a longitudinal latent class analysis. RESULTS: Three classes of differentially progressing early age DMD motor trajectories were identified. Quicker decline/progression was associated with lower baseline TFT velocities, earlier loss of ability to finish a TFT, and lower predicted velocities. Earlier substantial steroid exposure was associated with greater TFT velocities while the moderate progression class was observed to have the largest difference in performance between boys treated early with steroids vs. not. Sample size calculations with the class showing the largest treatment response showed a large reduction in required sample size as compared to using summaries from all participants. Gene mutations were also investigated in post-hoc analyses, with mutations near the beginning of the DMD gene (Dp427 absent and Dp140/Dp71 present) found to be enriched in the slowest progressing class. CONCLUSIONS: This study provides insight into the variation in DMD progression through a latent class analysis. Our findings show class-related trajectories of motor outcomes and pharmacological response to corticosteroids, and suggest that enrichment strategies and/or subgroup analyses could be considered further in design of therapeutic interventions in DMD.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".