D.08 ACT DMD (Ataluren Confirmatory Trial in Duchenne Muscular Dystrophy): effect of Ataluren on timed function tests (TFT) in nonsense mutation (nm) DMD
Bibliographic record
Abstract
Background: Ataluren is the first drug to treat the underlying cause of nmDMD. Methods: ACT DMD is a Phase 3, randomized, double-blind study. Males 7-16 years with nmDMD and a screening six-minute walk distance (6MWD) ≥150 m and <80%-predicted were randomized to ataluren 40 mg/kg/day or placebo for 48 weeks. A pre-specified subgroup included patients with baseline 6MWD 300-400 m. A meta-analysis of the overall ACT DMD population and the ‘ambulatory decline phase’ subgroup of the Phase 2b study (those patients meeting ACT DMD entry criteria) was pre-specified in the statistical plan. Results: In the overall ACT DMD population (N=228), changes in TFTs favored ataluren over placebo: 10-meter walk/run, -1.2s (p=0.117); 4-stair climb, -1.8s (p=0.058); 4-stair descend, -1.8s (p=0.012). In the pre-specified subgroup (n=99), these differences increased to -2.1s, -3.6s, and -4.3s, respectively, and were statistically significant (p<0.01) for 4-stair climb and descend. Results are supported by the meta-analysis (N=291), which demonstrated significant differences (p<0.05) in 10-meter walk/run, 4-stair climb, 4-stair descend. Conclusions: TFT results showed a benefit for ataluren in ACT DMD, and a larger treatment effect in the pre-specified baseline 6MWD 300-400 m subgroup as well as the pre-specified meta-analysis of ACT DMD and the Phase 2b study decline subgroup. Supported By: PTC Therapeutics Inc.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".