Pediatric Sarcopenia: A Paradigm in the Overall Definition of Malnutrition in Children?
Bibliographic record
Abstract
BACKGROUND: Malnutrition is a common complication in children with chronic diseases. Sarcopenia is one component of malnutrition, characterized by reduced skeletal muscle mass (SMM) and muscle function. The presence of sarcopenia is associated with adverse outcomes in children. Although there is growing research interest in sarcopenia, no review has been done on this novel concept in pediatrics. The purpose of this review was to explore current evidence in sarcopenia with and without obesity and to evaluate the knowledge gaps in the assessment of childhood sarcopenia. METHODS: A total of 12 articles retrieved from PubMed or Web of Science databases were included. RESULTS: Limited studies have elucidated sarcopenia in pediatrics. Challenges in sarcopenia assessment include heterogeneity in definition and absence of standardized body composition methods used to measure SMM and muscle function tests. There is a lack of age-specific and gender-specific normative data for SMM, particularly in young children and infants. None of the studies incorporated muscle function assessment, causing potential bias and misclassification of sarcopenia. The research in childhood sarcopenia is also hampered by low study quality, limited number of outcomes-based research, and lack of longitudinal data. CONCLUSION: Consensus needs to be reached in methodological approaches in sarcopenia diagnosis, body composition measurements, and age-appropriate muscle function tests in pediatrics. Careful considerations on growth, neurocognitive status, and factors influencing development in various clinical populations are warranted. Early identification of sarcopenia is crucial to enable targeted treatment and prevention to be carried out across the pediatric clinical populations.
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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 | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".