Impact of obesity on outcome in children diagnosed with cancer in Canada: A report from Cancer in Young People in Canada
Bibliographic record
Abstract
BACKGROUND: Childhood obesity can result in adverse health outcomes. The objectives of this study were to describe the prevalence of obesity and determine the association between obesity at cancer diagnosis and event-free survival (EFS) and overall survival (OS) in children diagnosed with cancer in Canada. METHODS: The authors conducted a retrospective cohort study using the Cancer in Young People in Canada database, including all children with newly diagnosed cancer aged 2-18 years across Canada from 2001 to 2020. Obesity was defined as age-adjusted and sex-adjusted body mass index greater than or equal to the 95th percentile. Univariate and multivariable Cox proportional hazards models compared EFS and OS between patients with and without obesity at diagnosis. RESULTS: In total, 11,291 patients were included, of whom 10.5% were obese at diagnosis. In multivariable models controlling for age, sex, ethnicity, neighborhood income quintile, treatment era, and cancer categories, obesity at diagnosis was independently associated with inferior EFS (adjusted hazard ratio [aHR], 1.16; 95% confidence interval [CI], 1.02-1.32; p = .02) and OS (aHR, 1.29; 95% CI, 1.11-1.49; p = .001). The adverse prognostic impact of obesity was particularly notable for acute lymphoblastic leukemia (ALL) and central nervous system (CNS) tumors. In children with ALL (n = 3458), obesity remained associated with inferior EFS (aHR, 1.55; p = .002) and OS (aHR, 1.75; p = .002) in multivariable analysis. In patients with CNS tumors (n = 2458), obesity was also associated with inferior EFS (aHR, 1.38; p = .008) and OS (aHR, 1.47; p = .004). CONCLUSIONS: In this population-based study, obesity at cancer diagnosis was independently associated with inferior survival across the entire cohort, and prominently in children with ALL and CNS tumors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".