Challenges and physiological implications of sarcopenia in children and youth in health and disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: Highlight the controversies and challenges associated with a sarcopenia diagnosis in infants and children and the potential physiological mechanisms contributing to this disorder. RECENT FINDINGS: Sarcopenia has been recently identified in infants and children with chronic diseases such as liver, cardiac, gastrointestinal, cancer and organ transplant recipients. However, there is no consensus regarding the definition of pediatric sarcopenia. Different sarcopenic phenotypes (sarcopenia and sarcopenic obesity) have been identified in healthy children and children with chronic disease. Both conditions have been associated with adverse clinical outcomes (e.g. delayed growth, increased hospitalization) in children and youth with chronic disease. The etiology of pediatric sarcopenia is likely multifactorial associated with malnutrition, physical inactivity and altered metabolic environments influencing skeletal muscle mass accumulation and function. Gaps in the literature include the lack of standard tools that should be used for the evaluation of skeletal muscular fitness and body composition in sarcopenia, particularly in infants and young children (<4years). SUMMARY: Longitudinal evaluation of sarcopenia expression and the underlying physiological and lifestyle factors contributing to pediatric sarcopenia are important to understand to ensure effective rehabilitation strategies can be developed and to avoid the adverse clinical consequences in children.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".