Overweight, obesity and adiposity in survivors of childhood brain tumours: a systematic review and meta‐analysis
Bibliographic record
Abstract
Survivors of childhood brain tumours (SCBT) have increased cardiometabolic risks, but the determinants of these risks are unclear. This systematic review aims to compare the prevalence of overweight and obesity as well as adiposity measures between SCBT and non-cancer controls. The PubMed, EMBASE, MEDLINE, CINAHL and the Cochrane Library databases were searched. The primary outcomes were the prevalence of overweight and obesity based on body mass index. The secondary outcomes were adiposity measures including percent fat mass, waist-to-hip and waist-to-height ratios. Forty-one studies were included in the meta-analysis. The prevalence of overweight and obesity combined was similar between overall SCBT, SCBT excluding craniopharyngioma and non-cancer controls (42.6%, 95% CI 30.1-55.1 vs. 31.7%, 95% CI 20.4-43.0 vs. 40.4%, 95% CI 34.0-46.8). We also found that SCBT have higher percent fat mass (mean difference 4.1%, 95% CI 2.0-6.1), waist-to-hip ratio (mean difference 0.07, 95% CI 0.02-0.13) and waist-to-height ratio (mean difference 0.06, 95% CI 0.01-0.10) than non-cancer controls. We conclude that SCBT have similar overweight and obesity distribution but higher adiposity than non-cancer controls. More studies were needed to explore the determinants of adiposity and its contribution to cardiometabolic outcomes in SCBT.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.025 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 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".