Adiposity in Survivors of Cancer in Childhood: How is it Measured and Why Does it Matter?
Bibliographic record
Abstract
Survival of cancer in childhood is increasingly common with modern therapeutic protocols but leads frequently to adverse long-term impacts on health, including metabolic and cardiovascular disease. Changes in body composition, especially an increase in fat mass and a decrease in muscle mass, are found early in patients with pediatric cancer, persist long after treatment has been completed and seem to contribute to the development of chronic disease. This review details the effects of such changes in body composition and reviews the underlying pathophysiology of the development of sarcopenic obesity and its adverse metabolic impact. The authors discuss the particular challenges in identifying obesity accurately in survivors of pediatric cancer using available measurement techniques, given that common measures, such as body mass index, do not distinguish between muscle and adipose tissue or assess their distribution. The authors highlight the importance of a harmonized approach to the assessment of body composition in pediatric cancer survivors and early identification of risk using "gold-standard" measurements. This will improve our understanding of the significance of adiposity and sarcopenia in this population, help identify thresholds predictive of metabolic risk, and ultimately prevent or ameliorate the long-term metabolic and cardiovascular impacts on health experienced by survivors of cancer in childhood.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".