Assessment of musculoskeletal abnormalities in children with mucopolysaccharidoses using pGALS
Bibliographic record
Abstract
BACKGROUND: Children with mucopolysaccharidoses (MPS) often have musculoskeletal (MSK) abnormalities. Paediatric Gait, Arms, Legs, and Spine (pGALS), is a simple MSK assessment validated in school-age children to detect abnormal joints. We aimed to identify MSK abnormalities in children with MPS performing pGALS. METHODS: Videos of children with a spectrum of MPS performing pGALS were analysed. A piloted proforma to record abnormalities for each pGALS manoeuvre observed in the videos (scored as normal/abnormal/not assessable) was used by three observers blinded to MPS subtype. Videos were scored independently and rescored for intra- and inter-observer consistency. Data were pooled and analysed. RESULTS: Eighteen videos of children [12 boys, 6 girls, median age 11 years (4-19)] with MPS (13 type I [5 Hurler, 8 attenuated type I]; 4 type II; 1 mannosidosis) were assessed. The most common abnormalities detected using pGALS were restrictions of the shoulder, elbow, wrist, jaw (>75% cases), and fingers (2/3 cases). Mean intra-observer Κ 0.74 (range 0.65-0.88) and inter-observer Κ 0.62 (range 0.51-0.77). Hip manoeuvres were not clearly demonstrated in the videos. CONCLUSIONS: In this observational study, pGALS identifies MSK abnormalities in children with MPS. Restricted joint movement (especially upper limb) was a consistent finding. Future work includes pGALS assessment of the hip and testing pGALS in further children with attenuated MPS type I. The use of pGALS and awareness of patterns of joint involvement may be a useful adjunct to facilitate earlier recognition of these rare conditions and ultimately access to specialist care.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".