Validity and predictability of mid-upper arm circumference for nutrition screening in outpatient preschoolers with cerebral palsy
Bibliographic record
Abstract
Aim: Evaluating the nutritional status of children with cerebral palsy (CP) is difficult due to spasticity and contractures. Mid-upper arm circumference (MUAC) is a potential screening tool for malnutrition in children with CP, but its effectiveness is unproven. This study aims to provide evidence on the psychometric qualities of MUAC for clinical use and establish optimal cutoffs for preschoolers with CP. Methods: = 937) were recruited from 24 hospitals across 13 provinces in China for the cross-sectional study, while those had genetic or metabolic diseases were excluded. Weight, length/height and MUAC were obtained from participants. Weight and length/height were calculated into Z scores by using WHO Anthro software to assess the nutritional status. The sensitivity and specificity of the WHO-recommended MUAC cutoffs were calculated. The Spearman's rank correlation, Receiver operating characteristic (ROC) curve, and Youden Index were conducted to establish the optimal MUAC cutoffs for preschoolers with CP. Results: = 0.575). The optimal MUAC cutoffs for mild, moderate, and severe undernutrition were 15.35, 15.05, and 14.35 cm, respectively; the optimal cutoffs for overweight and obesity were 17.55 and 20.4 cm, respectively. Conclusion: Our study suggests that MUAC is a useful tool for screening the nutritional status of children with CP. However, the WHO-recommended MUAC cut-off may not be suitable for preschool with CP. We estimated that the optimal MUAC cutoffs were 15.35 cm for mild undernutrition, 15.05 cm for moderate undernutrition, and 14.35 cm for severe undernutrition, and 17.55 cm for overweight and 20.4 cm for obesity in preschool with CP. Clinical trial registration: www.chictr.org.cn, ChiCTR2000033869.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".