PS1 - 176 Where Have All the Fat Cells Gone? A Comparative Analysis of Adiposity Patterns in Childhood Brain Tumor Survivors and Non-Cancer Controls
Bibliographic record
Abstract
Brain tumors are the most common solid tumors in children in Canada. While technological advances have increased their survival rates, survivors of childhood brain tumors (SCBT) often develop obesity, which can reduce lifespan and quality of life. While adiposity is a known factor for cardiometabolic disorders in the general population, adiposity patterns in SCBT have not been determined. This study aims to investigate how adiposity patterns differ between SCBT and non-cancer controls, and how lifestyle and treatment factors may contribute to these patterns. Methods: Fifty-nine SCBT and 108 non-cancer controls were recruited from the clinics at McMaster Children’s Hospital. Sociodemographic and lifestyle details were collected using standardized tools to assess diet, physical activity, and sleep. Brain tumor type, location and treatment details were obtained from medical records. Total and visceral adiposity were determined by total fat mass (FM) as well as waist-to-hip (WHR) and waist-to-height ratio (WHTR). Results: SCBT have higher total and visceral adiposity, while BMI is similar to controls. Female SCBT who received radiotherapy and/or chemotherapy have higher adiposity. A dietary pattern of white bread and fried foods with low dark bread was positively associated with adiposity. Lower physical activity levels, but not sleep durations, were associated with higher adiposity. Conclusion: SCBT have higher visceral and total adiposity than non-cancer controls. Sex, chemoradiotherapy, high fat diet, and physical inactivity, can contribute to these adiposity patterns. These results provide multiple points of entry to design interventions that reduce adiposity, and may improve long-term 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 | 0.000 | 0.001 |
| 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) | 0.004 | 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".