Novel approaches to analysis of the North Star Ambulatory Assessment (NSAA) in Duchenne muscular dystrophy (DMD): Observations from a phase 2 trial
Bibliographic record
Abstract
INTRODUCTION: The North Star Ambulatory Assessment (NSAA) tool is a key instrument for measuring clinical outcomes in patients with Duchenne muscular dystrophy (DMD). To gain a better understanding of the longitudinal utility of the NSAA, we evaluated NSAA data from a phase II trial of 120 patients with DMD treated with domagrozumab or placebo. METHODS: The NSAA exploratory analyses included assessment of individual skills gained/lost, total skills gained/lost, cumulative loss of function, and the impact of transient loss of function due to a temporary disability on NSAA total score (temporary zero score). RESULTS: There was no significant difference in the total number of NSAA skills gained (mean 1.41 and 1.04, respectively; p = 0.3314) or lost (3.90 vs. 5.0; p = 0.0998) between domagrozumab- vs. placebo-treated patients at week 49. However, domagrozumab-treated patients were less likely to lose the ability to perform a NSAA item (hazard ratio 0.80, 95% confidence interval [CI]: 0.65-0.98, p = 0.029) over 48-weeks vs. placebo-treated patients. When temporary zero scores were changed to "not obtainable" (8 values from 7 patients), domagrozumab-treated patients scored higher on the NSAA total score versus placebo-treated patients (difference at week 49: 2.0, 95% CI: 0.1-3.9, p = 0.0359). CONCLUSIONS: These exploratory analyses reveal additional approaches to interpreting the NSAA data beyond just change in NSAA total score. These observations also highlight the importance of reporting items as "not obtainable" for a patient with a temporary/transient physical disability that impacts their ability to perform the NSAA test. CLINICALTRIALS.GOV IDENTIFIER: NCT02310763.
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 | 0.040 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".