Deflazacort vs prednisone treatment for Duchenne muscular dystrophy: A meta‐analysis of disease progression rates in recent multicenter clinical trials
Bibliographic record
Abstract
INTRODUCTION: In this study we characterized disease progression over 48 weeks among boys receiving deflazacort vs prednisone/prednisolone placebo arm treatment in two recent Duchenne muscular dystrophy (DMD) clinical trials. METHODS: Ambulatory boys with DMD receiving placebo in the phase 3 ataluren (N = 115) and tadalafil (N = 116) trials were included. The trials required at least 6 months of prior corticosteroid use and stable baseline dosing. Associations between corticosteroid use and 48-week changes in ambulatory function were estimated using mixed models. Adjusted differences between corticosteroid groups were pooled in a meta-analysis. RESULTS: In the meta-analysis, deflazacort-treated patients vs prednisone/prednisolone-treated patients experienced, on average, lower declines of 28.3 meters on 6-minute walk distance (95% confidence interval [CI], 5.7, 50.9; 2.9 seconds on rise from supine [95% CI, 0.9, 4.9 seconds]; 2.3 seconds on 4-stair climb [95% CI, 0.5, 4.1 seconds]; and 2.9 [95% CI, 0.1, 5.8] points on the North Star Ambulatory Assessment linearized score). DISCUSSION: Deflazacort-treated patients experienced significantly lower functional decline over 48 weeks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.035 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".