Longitudinal changes in skeletal muscle in children undergoing cancer treatment: a systematic review and meta-analysis
Bibliographic record
Abstract
Skeletal muscle loss during chemotherapy has been associated with poorer outcomes and reduced survival across several types of cancer. However, the extent and progression of muscle loss during treatment for childhood cancers remain unclear. A better understanding could help identify children at increased risk and inform the timing of targeted intervention. This systematic review and meta-analysis aimed to synthesize the evidence on skeletal muscle changes during treatment for childhood cancers and identify factors that influence these outcomes. A systematic search was conducted in CINAHL, Embase, PubMed, SPORTDiscus, and Web of Science. Studies were eligible if they included children and adolescents (< 19 years) undergoing cancer treatment and reported muscle quantity at a minimum of two time points. The methodological quality of the included studies was evaluated using the Newcastle-Ottawa Scale. Twenty studies (n = 646; age range: 2.5-14.7 years) were included. A significant decline in muscle quantity was observed during the early phase of treatment (standardized mean difference (SMD): SMD = - 0.36; 95% CI: - 0.59 to - 0.13; p < 0.05). At later follow-up time points, the overall change was not statistically significant (SMD = - 0.08; 95% CI: - 0.27 to 0.10; p = 0.36). However, estimates of muscle quantity varied significantly by assessment modality (p = 0.048). CONCLUSION: Children with cancer experience significant skeletal muscle loss during the intensive phase of treatment. While decrements observed at later time points appear modest, reported outcomes vary considerably depending on the assessment method. Standardized, reliable body composition measures are needed to detect meaningful changes and guide clinical care. WHAT IS KNOWN: • Skeletal muscle loss during adult cancer treatment is linked to poor outcomes, yet pediatric data are limited and inconsistent. WHAT IS NEW: • This is the first systematic review and meta-analysis showing significant early-phase skeletal muscle loss in children undergoing cancer treatment, accompanied by concurrent increases in fat mass. • Considerable variability in skeletal muscle estimates across different body composition assessment methods underscores the need for reliable and sensitive measurement techniques to monitor changes accurately and guide clinical care.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.033 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".