Dual-energy X-ray absorptiometry measures of lean body mass as a biomarker for progression in boys with Duchenne muscular dystrophy
Bibliographic record
Abstract
We evaluated whether whole-body dual-energy X-ray absorptiometry (DXA) measures of lean body mass can be used as biomarkers for disease progression and treatment effects in patients with Duchenne muscular dystrophy. This post hoc analysis utilized data from a randomized, 2-period study of domagrozumab versus placebo in 120 ambulatory boys with DMD. DXA measures of lean body mass were obtained from the whole body (excluding head), arms, legs and appendicular skeleton at baseline and every 16 weeks. Treatment effects on DXA measures for domagrozumab versus placebo were assessed at Week 49. At Week 49, domagrozumab statistically significantly increased lean body mass versus placebo in the appendicular skeleton (p = 0.050) and arms (p < 0.001). The relationship between lean body mass at Week 49 and functional endpoints at Week 97 was evaluated. Changes in lean body mass at Week 49 in all regions except arms were significantly correlated with percent change from baseline in 4-stair climb (4SC) at Week 97. DXA-derived percent lean mass at Week 49 also correlated with 4SC and North Star Ambulatory Assessment at Week 97. These data indicate that whole-body DXA measures can be used as biomarkers for treatment effects and disease progression in patients with DMD, and warrant further investigation.Trial registration: ClinicalTrials.gov, NCT02310763; registered 8 December 2014.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".