A Characterization of Movement Skills in Obese Children With and Without Prader-Willi Syndrome
Bibliographic record
Abstract
PURPOSE: The aim of this study was twofold: (a) to measure and compare motor proficiency in obese children with Prader-Willi syndrome (OB-PWS) to that in obese children without PWS (OB), and (b) to compare motor proficiency in OB-PWS and OB to normative data. METHOD: Motor proficiency was measured using the Bruininks-Oseretsky Test of Motor Proficiency-Second Edition-Complete Form, a norm-referenced assessment of motor function. Participants were 18 OB-PWS and 44 OB (8 to 11 years of age). The scores on the 8 subtests and the total motor composite were used to compare OB-PWS and OB. Furthermore, the scores on the 4 motor-area composites were used to compare OB-PWS and OB against normative data. RESULTS: OB-PWS scored significantly lower than OB across all 8 subtests. OB-PWS also had significantly lower motor proficiency scores on all motor-area composites when compared with the normative sample. OB-PWS most frequently (67%-83%) scored well below average on the gross motor subtests. Although not as high as on the gross motor subtests, this finding also held true for the fine motor subtests with 39% to 45% of OB-PWS scoring well below average. CONCLUSION: Motor proficiency is very poor in OB-PWS, in particular for gross motor skills. Physical, neurological, and developmental characteristics inherent to the syndrome may explain this low performance.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".