The NorthStar Ambulatory Assessment in Duchenne muscular dystrophy: considerations for the design of clinical trials
Bibliographic record
Abstract
OBJECTIVE: With the emergence of experimental therapies for Duchenne muscular dystrophy (DMD), it is fundamental to understand the natural history of this disorder to properly design clinical trials. The aims of this study were to assess the effects produced on motor function by different DMD genotypes and early initiation of glucocorticoids. METHODS: Through the NorthStar Network, standardised clinical data including the NorthStar Ambulatory Assessment score (NSAA) on 513 ambulant UK boys with DMD were analysed from 2004 to 2012. For the analysis of the genetic subpopulation, we also included data from 172 Italian boys with DMD. NSAA raw scores were converted into linear scores. RESULTS: On the linearised NSAA, we observed an average decline of 8 units/year (4 units on raw NSAA analysis) after age 7. The median age at loss of ambulation (LOA) was 13 years (95% CI 12.1 to 13.5); 2 years prior to LOA, the estimated mean linearised NSAA score was 42/100 (13/34 raw scale). Starting glucocorticoids between 3 and 5 years conferred an additional gain in motor function of 3 units/year (1.3 raw units) up to age 7. When analysing the effect of genotype in the UK and Italian cumulative cohorts, individuals with deletions amenable to exons 44 and 46 skipping declined at a slower rate over 2 years (9 units (4 raw units), p<0.001), while 53 and 51 skippable deletions showed a faster decline of 14 (4.5; p<0.001) and 5 linearised units (2.4 NSAA units; p=0.02), respectively. CONCLUSIONS: Our study provides a novel insight on the current natural history of DMD, which will be instrumental for the design of future clinical trials.
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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 | 0.348 | 0.322 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".