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Prevalence of obesity and its impact on outcome in children diagnosed with cancer in Canada: A population-based study.

2024· article· en· W4399281563 on OpenAlexafffundabout
Samuel Sassine, Hallie Coltin, Henrique Bittencourt, Uma H. Athale, Lynette Bowes, Josée Brossard, Sara J. Israels, Ketan Kulkarni, Sarah McKillop, Meera Rayar, Roona Sinha, Tony H. Truong, Donna L. Johnston, Catherine Vézina, Laura Wheaton, Alexandra P. Zorzi, Lillian Sung, Marie‐Claude Pelland‐Marcotte, Thai Hoa Tran

Bibliographic record

VenueJournal of Clinical Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsHospital for Sick ChildrenKingston Health Sciences CentreMontreal Children's HospitalChildren's Hospital of Eastern OntarioAlberta Children's HospitalStollery Children's HospitalIzaak Walton Killam Health CentreBC Children's HospitalCentre Hospitalier Universitaire Sainte-JustineDalhousie UniversitySaskatchewan Cancer AgencyCancerCare ManitobaJaneway Children's Health and Rehabilitation CentreCentre Hospitalier Universitaire de SherbrookeLondon Health Sciences CentreMcMaster Children's Hospital
FundersFonds de Recherche du Québec - SantéLeukemia and Lymphoma Society of Canada
KeywordsMedicineObesityCancerPopulationOutcome (game theory)PediatricsDemographyEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

10517 Background: Childhood obesity can result in a variety of adverse health outcomes. Objectives were to describe the prevalence of obesity and determine the association between baseline obesity with event-free survival (EFS) and overall survival (OS) in children diagnosed with cancer in Canada. Methods: We conducted a retrospective cohort study using the Cancer in Young People in Canada database, a population-based surveillance program collecting data from all cancer patients < 15 years old before 2015 and < 19 years old after 2015. This study included all children with newly diagnosed cancer aged 2 to 18 years across Canada from 2001 to 2020. Obesity was defined as age and sex-adjusted body mass index ≥ 95th percentile. EFS was defined as the time from diagnosis to first event (relapse, progression, secondary malignancies, or death). Univariate and multivariable Cox proportional hazards models compared EFS and OS between patients with and without obesity. The prevalence of obesity was compared annually from diagnosis. All analyses were stratified by cancer categories. Results: A total of 11,291 patients were included (37.1% leukemias, 14.5% lymphomas, 21.8% central nervous system (CNS) tumors, 26.6% non-CNS solid tumors; median age: 7.6 years [interquartile range (IQR): 4.2-12.5] and median follow-up time: 4.7 years [IQR: 2.6-5.1]). At diagnosis, 10.5% were obese and the prevalence of obesity was significantly higher at 1 year (15.7%, p < 0.001) and 2 years (20.1%, p < 0.001) after diagnosis. The prevalence of obesity was higher in the leukemia and lymphoma cohort compared to other cancer categories (11.4% vs. 9.5%, p = 0.001). In multivariable models controlling for age at diagnosis, sex, ethnicity, income quintile, treatment era and cancer categories, obesity at diagnosis remained significantly associated with inferior EFS [adjusted HR (aHR) 1.16, 95% CI 1.02–1.32] and OS [aHR 1.29, 95% CI 1.11–1.49]. In children with acute lymphoblastic leukemia (ALL) (n = 3458, 82.5% of all leukemias), after adjusting for high-risk features such as age, white blood cell count and CNS status, obesity at diagnosis remained associated with inferior EFS [aHR 1.55, 95% CI 1.17–2.04] and OS [aHR 1.75, 95% CI 1.23–2.49]. In patients with CNS tumors, obesity at diagnosis was also associated with inferior EFS [aHR 1.38, 95% CI 1.09–1.76] and OS [aHR 1.47, 95% CI 1.13–1.91]. Obesity at diagnosis did not confer adverse outcomes in other cancer categories. Conclusions: In this population-based study of children with cancer, the prevalence of obesity was 11% at diagnosis and continued to increase in the following 2 years. Obesity at cancer diagnosis was independently associated with inferior survival across the entire cohort, especially in children with ALL and CNS tumors. Further studies are needed to better characterize the relationship between obesity and survival in children with cancer to inform future trial design.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.004
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.077
GPT teacher head0.480
Teacher spread0.403 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2024
Admission routes3
Has abstractyes

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