Predicting trajectories of the north star ambulatory assessment total score in Duchenne muscular dystrophy
Bibliographic record
Abstract
The North Star Ambulatory Assessment (NSAA) is a widely used functional endpoint in drug development for ambulatory patients with Duchenne muscular dystrophy (DMD). Accurately predicting NSAA total score trajectories is important for designing randomized trials for novel therapies in DMD and for contextualizing outcomes, especially over longer-term follow-up (>18 months) when placebo-controlled studies are infeasible. We developed a prognostic model for NSAA total score trajectories over at most 5 years of follow-up for patients with DMD aged 4 to <16 years who were initially ambulatory and receiving corticosteroids but no other disease-modifying therapies. The model was based on longitudinal data from four natural history databases: UZ Leuven, PRO-DMD-01 (provided by CureDuchenne), the North Star Clinical Network, and iMDEX. Candidate predictors included age, height, weight, body mass index, steroid type and regime, NSAA total score, rise from floor velocity, and 10-meter walk/run velocity, as well as DMD genotype class, index year, and data source. Among N = 416 patients at baseline, mean age was 8.2 years, mean NSAA total score was 24, and 61% were receiving prednisone and 39% deflazacort, with the majority having been treated with daily corticosteroid regimens (69%) relative to other regimens (31%). Patients had an average of four NSAA assessments post-baseline during a median follow-up of 2.6 years (inter-quartile range 1.9 to 3.6 years). The best-fitting model in the full study sample explained 39% of the variation in NSAA total score changes, with prediction errors of ±3.6, 5.1, 5.9, 7.5, 9.5 NSAA units during follow-up years 1-5, respectively. The most important predictors were baseline age, NSAA, rise from floor velocity, and 10-meter walk/run velocity. In conclusion, trajectories of ambulatory motor function in DMD, as measured by the NSAA total score, can be well-predicted using readily available baseline characteristics. We discuss applications of these predictions to DMD drug development.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".