Sarcopenia in long‐term survivors of cancer in childhood and adolescence: A cross‐sectional study of calf muscle mass by peripheral quantitative computed tomography with an examination of the muscle–bone unit
Bibliographic record
Abstract
BACKGROUND: Sarcopenia, a loss of systemic skeletal muscle mass (SMM), is prevalent in childhood cancer survivors and often accompanied by increased fat mass (sarcopenic obesity [SO]). We examined whether calf muscle cross-sectional area (CSA), measured by peripheral quantitative computed tomography (pQCT), can identify sarcopenia and SO in long-term survivors of pediatric acute lymphoblastic leukemia (ALL), and explored its relationship with adjacent bone geometry. PROCEDURES: Calf muscle CSA and CSA Z scores at the 38% tibia in 70 subjects (median survival 15 years) were compared between sexes and ALL risk group, and their association with sarcopenia and SO evaluated. Relationships between bone strength and muscle mass were assessed further using linear regression and methods described by Schoenau et al. for evaluation of a functional muscle-bone unit. RESULTS: A calf muscle CSA Z score of less than or equal to zero is 73.81% sensitive and 84.62% specific for detecting sarcopenia defined by height-adjusted appendicular lean mass measured by dual-energy x-ray absorptiometry. No significant difference in CSA Z scores was noted between sexes or ALL risk groups; however, Z scores were markedly higher in those without SO (adjusted odds ratio 0.529, 95% confidence interval [CI]: 0.340-0.824). Ratios of calf muscle mass to height and total bone mineral content at the 38% tibia are "sufficient" and consistent with estimated reference ranges for a healthy population. CONCLUSIONS: CSA Z scores may represent a useful clinical measure of SMM and are predictive of SO in this population. Our results are supportive of a functional muscle-bone unit in long-term survivors of pediatric ALL.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".