Joint Modelling of Growth and Motor Function Centiles in Corticosteroids Treated Boys With Duchenne Muscular Dystrophy
Bibliographic record
Abstract
BACKGROUND: Corticosteroid (CS) treated boys with DMD display higher rates of height stunting, higher weight gain, improved motor function scores and delayed loss of ambulation compared to untreated patients. However, the relationship between growth and motor function has historically been understudied due to modelling complexities. METHODS: In this analysis, we use the newly developed motor function centiles for the NSAA, RFF and 10MWR. We consider each combination of growth (height and weight SD) and motor function using multivariate regression models controlling for differential CS treatment (prednisolone/deflazacort, daily/intermittent). This allows inference on the growth and motor function outcomes separately and on the relationship between the outcomes. RESULTS: We consider 559 steroid-treated boys with DMD between the ages of 5 and 16 over 1643 assessments. Better motor function trajectories were observed in those treated with daily CS, with the deflazacort daily group displaying a positive NSAA centile trajectory (annual change of 0.07 SD). There was a mild, negative pairwise correlation between the annual changes in NSAA and 10MWR Z-Scores, and height and weight Z-Scores, ranging from -0.25 to -0.36. This indicated that patients with a milder weight gain or more severe height stunting trajectory with respect to their CS treatment were more likely to exhibit a more favourable NSAA or 10MWR trajectory over time. CONCLUSIONS: This work describes the complex relationships between motor function, CS treatment and growth and provides insights for conversations about the relative benefits and negative effects of CS.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".