Growth assessment and weight management in paediatric neuromuscular clinics: a cross-sectional survey across Canada
Bibliographic record
Abstract
To identify current practices related to the assessment, monitoring and discussion of bodyweight, growth and obesity in neuromuscular clinics for children with Duchenne muscular dystrophy (DMD). A cross-sectional, online survey was distributed using snowball sampling to healthcare providers working with children with DMD across Canadian neuromuscular clinics. Summary and descriptive statistics were calculated. Content analysis was performed on open text responses. Thirty-seven responses were received, representing a range of healthcare disciplines. Height and weight were routinely assessed by 32/37 (87%) respondents, although only 21/37 (57%) responses reported having a clinic standard for measuring height and 23/37 (62%) for weight. While 32/36 (89%) reported discussing weight during consultations, only 13/37 (35%) felt confident doing so. Dietitians were considered the most appropriate person to discuss and manage weight with children and families, although only 17/37 (46%) reported having a dietitian involved in their clinic. Neuromuscular clinics could benefit from implementing consistent and recommended growth assessment practices. The development of evidence-based tools, training and protocols tailored to Duchenne muscular dystrophy should be a priority.IMPLICATIONS FOR REHABILITATIONGrowth and weight monitoring approaches vary within and between neuromuscular clinics.Additional training on discussing and managing weight are warranted across disciplines.Advocacy is required to ensure access to dietetic expertise within neuromuscular clinics. Growth and weight monitoring approaches vary within and between neuromuscular clinics. Additional training on discussing and managing weight are warranted across disciplines. Advocacy is required to ensure access to dietetic expertise within neuromuscular clinics.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".