Exercise rehabilitation to treat sarcopenia in pediatric transplant populations
Bibliographic record
Abstract
BACKGROUND: In adult transplant (Tx) populations, exercise rehabilitation strategies may improve sarcopenia components (muscle mass [MM], strength [MS], and physical performance [PP]). Limited data are available regarding exercise rehabilitation therapy in pediatric Tx populations. METHODS: The purpose of this review is to critically evaluate the feasibility and impact of exercise programs (EP) that include resistance exercise (RE) on markers of sarcopenia in pediatric Tx populations. Literature searches in SCOPUS and WEB OF SCIENCE were conducted to identify studies applying EP with a RE component in pediatric populations in the Tx setting. RESULTS: Twelve articles (2008-2022) met inclusion criteria. The exercise interventions varied in length (3 weeks-12 months), intensity (low to moderate), time pre/post Tx (0 days-5 years post Tx), age of participants (3-18 years), adherence (63%-94%), and methodologies to measure components of sarcopenia. No studies measured all three components of sarcopenia concurrently. Approximately, 60% of studies found positive effects on MS and PP. Only one pediatric study measured body composition, therefore, the effect of exercise programs with RE components on MM is unknown. CONCLUSIONS: Exercise programs may be a beneficial treatment for sarcopenia in Tx populations, particularly in components of MS and PP. Studies measuring all three aspects of sarcopenia together in response to RE training in pediatrics remains an important gap. Studies that include body composition measurements in response to exercise are needed. Special considerations for the development of RE programs in pediatrics Tx populations are safety, supervision, engagement through family/peer involvement and incorporation of game/play-based elements.
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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 | 0.003 | 0.007 |
| 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 | 0.000 |
| Research integrity | 0.001 | 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".