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Record W4409404236 · doi:10.1503/cmaj.241456

Managing obesity in children: a clinical practice guideline

2025· article· en· W4409404236 on OpenAlexafffundvenue
Geoff D.C. Ball, Roah Merdad, Catherine S. Birken, Tamara R. Cohen, Stasia Hadjiyannakis, Jill Hamilton, Mélanie Henderson, John Lammey, Katherine M. Morrison, Sarah A. Moore, Aislin R. Mushquash, Ian Patton, Nicole Pearce, Joshua Ramjist, Tracy Lebel, Brian W. Timmons, Annick Buchholz, Jennifer Cantwell, Jennifer Cooper, Julius Erdstein, Donna Fitzpatrick‐Lewis, Dawn Hatanaka, Patrice Lindsay, Tasneem Sajwani, Meghan Sebastianski, Diana Sherifali, Julie St‐Pierre, Muhammad Usman Ali, Jessica Wijesundera, Angela S. Alberga, Christine Ausman, Trisha C Baluyot, Emily Burke, Kara Dadgostar, Bronwyn Delacruz, Elizabeth Dettmer, Maegan Dymarski, Zahra Esmaeilinezhad, Ilona Hale, Soren Harnois‐Leblanc, Josephine Ho, Nicole D. Gehring, Marsha Kucera, Jacob C. Langer, Amy C. McPherson, Leen Naji, Krista Oei, Grace O’Malley, Angelica M Rigsby, Gita Wahi, Ian Zenlea, Bradley C. Johnston

Bibliographic record

VenueCanadian Medical Association Journal · 2025
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsUniversity of Toronto
FundersObesity CanadaAlberta Health Services
KeywordsGuidelineMedicineObesityComputer scienceData scienceFamily medicinePathology

Abstract

fetched live from OpenAlex

BACKGROUND: Obesity is a complex, chronic, stigmatized disease whereby abnormal or excess body fat may impair health or increase the risk of medical complications, and can reduce quality of life and shorten lifespan in children and families. We developed this guideline to provide evidence-based recommendations on options for managing pediatric obesity that support shared decision-making among children living with obesity, their families, and their health care providers. METHODS: We followed the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. We used the Guidelines International Network principles to manage competing interests. Caregivers, health care providers, and people living with obesity participated throughout the guideline development process, which optimized relevance. We surveyed end users (caregivers, health care providers) to prioritize health outcomes, completed 3 scoping reviews (2 on minimal important difference estimates; 1 on clinical assessment), performed 1 systematic review to characterize families' values and preferences, and conducted 3 systematic reviews and meta-analyses to examine the benefits and harms of behavioural and psychological, pharmacologic, and surgical interventions for managing obesity in children. Guideline panellists developed recommendations focused on an individualized approach to care by using the GRADE evidence-to-decision framework, incorporating values and preferences of children living with obesity and their caregivers. RECOMMENDATIONS: Our guideline includes 10 recommendations and 9 good practice statements for managing obesity in children. Managing pediatric obesity should be guided by a comprehensive child and family assessment based on our good practice statements. Behavioural and psychological interventions, particularly multicomponent interventions (strong recommendation, very low to moderate certainty), should form the foundation of care, with tailored therapy and support using shared decision-making based on the potential benefits, harms, certainty of evidence, and values and preferences of children and families. Pharmacologic and surgical interventions should be considered (conditional recommendation, low to moderate certainty) as therapeutic options based on availability, feasibility, and acceptability, and guided by shared decision-making between health care providers and families. INTERPRETATION: This guideline will support children, families, and health care providers to have informed discussions about the balance of benefits and harms for available obesity management interventions to support value- and preference-sensitive decision-making.

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)0.016
metaresearch head score (Gemma)0.065
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.065
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0070.006
Science and technology studies0.0020.002
Scholarly communication0.0040.004
Open science0.0060.004
Research integrity0.0100.008
Insufficient payload (model declined to judge)0.0060.004

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 head0.032
GPT teacher head0.474
Teacher spread0.442 · 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 designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations42
Published2025
Admission routes3
Has abstractyes

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