ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2025
Fully personalized modelling of Duchenne Muscular Dystrophy ambulation.
Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Fully personalized modelling of Duchenne Muscular Dystrophy ambulation.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Preface to the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 2)'.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
Duchenne Muscular Dystrophy is a progressive neuromuscular disorder characterized by the gradual weakening and deterioration of muscles, leading to loss of ambulation in affected individuals. This decline in mobility can be effectively assessed using the North Star Ambulatory Assessment (NSAA) scores, along with measures such as the 10-m walk time and the time taken to rise from the floor. We propose a dynamic linear model to predict the trajectories of these clinical outcomes, with a primary focus on NSAA scores. Our model aims to assist clinicians in forecasting the progression of the disease, thereby enabling more informed and personalized treatment plans for their patients. We also evaluate the effectiveness of our models in generating synthetic NSAA score datasets. We assess the performance of our modelling approach and compare the results with those of a previous study. We show that the most robust model demonstrates narrower prediction intervals and improved quantile coverage, indicating superior predictive accuracy and reliability.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 2)'.
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Registered trials
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