Course of joint range of motion in children with spinal muscular atrophy receiving disease-modifying treatment
Bibliographic record
Abstract
Abstract Background Progressive decreases in joint range of motion (ROM) is a well-recognized complication in the natural history of spinal muscular atrophy (SMA). How joint ROM evolves in children with SMA receiving disease-modifying treatment (DMT) needs to be documented. Purpose To examine the longitudinal course of joint range of motion in young children with SMA receiving disease-modifying therapy. Methods We included children with SMA (with 2 or 3 SMN2 copies) who started treatment within the first 18 months of life in a prospective national tertiary cohort study. Our examination consisted of joint range of motion of the knee, elbow and wrist; the longitudinal course was studied using linear mixed-effects models. Results We analysed 165 visits of 39 children (median age 22 months (interquartile range [6–45])) with treated SMA over a 3-year follow-up period. The median age at start of treatment was 2 months [0–8]. We found an average yearly decline in knee extension mobility of 3°. The overall course of range of motion for elbow and wrist remained stable. Conclusion The course of joint mobility in children with SMA, who started treatment with DMT in the first 18 months of life, is characterised by a decline in knee extension and a stable range of motion of wrist and elbow joints. We stress the importance of monitoring knee extension range of motion at least every 6 months and adopting a proactive approach to maintain full knee extension for optimal lifelong mobility.
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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 | 0.001 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".