MétaCan
Menu
Back to cohort
Record W3084194636 · doi:10.1136/bmjnph-2020-000074

Sex and gender differences in childhood obesity: contributing to the research agenda

2020· article· en· W3084194636 on OpenAlexaff
Bindra Shah, Katherine Tombeau Cost, Anne Fuller, Catherine S. Birken, Laura N. Anderson

Bibliographic record

VenueBMJ Nutrition Prevention & Health · 2020
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsHospital for Sick ChildrenUniversity of TorontoSickKids FoundationInstitute for Clinical Evaluative SciencesImpactMental Health Research CanadaMcMaster University
Fundersnot available
KeywordsObesityChildhood obesityPublic healthSex characteristicsDemographyMedicineEnvironmental healthPsychologyOverweightSociologyEndocrinology

Abstract

fetched live from OpenAlex

Childhood obesity is a major public health challenge and its prevalence continues to increase in many, but not all, countries worldwide. International data indicate that the prevalence of obesity is greater among boys than girls 5-19 years of age in the majority of high and upper middle-income countries worldwide. Despite this observed sex difference, relatively few studies have investigated sex-based and gender-based differences in childhood obesity. We propose several hypotheses that may shape the research agenda on childhood obesity. Differences in obesity prevalence may be driven by gender-related influences, such as societal ideals about body weight and parental feeding practices, as well as sex-related influences, such as body composition and hormones. There is an urgent need to understand the observed sex differences in the prevalence of childhood obesity; incorporation of sex-based and gender-based analysis in all childhood obesity studies may ultimately contribute to improved prevention and treatment.

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)0.061
metaresearch head score (Gemma)0.085
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: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.061
Threshold uncertainty score0.322

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0610.085
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0050.007
Science and technology studies0.0030.009
Scholarly communication0.0060.013
Open science0.0040.005
Research integrity0.0080.008
Insufficient payload (model declined to judge)0.0140.001

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 head0.156
GPT teacher head0.420
Teacher spread0.264 · 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
GenreCommentary

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

Citations221
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueBMJ Nutrition Prevention & HealthSame topicObesity, Physical Activity, DietFrench-language works237,207