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Record W2910259443 · doi:10.1139/cjpp-2018-0403

Prenatal determinants of childhood obesity: a review of risk factors

2019· review· en· W2910259443 on OpenAlexaffvenue
Xiang‐Peng Liao, Yamei Yu, Isabelle Marc, Lise Dubois, Nadia Abdelouahab, Luigi Bouchard, Yanting Wu, Fengxiu Ouyang, Heng-Feng Huang, William D. Fraser

Bibliographic record

VenueCanadian Journal of Physiology and Pharmacology · 2019
Typereview
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-JeanCentre hospitalier de l'Université LavalUniversity of OttawaUniversité de SherbrookeCentre hospitalier universitaire de QuébecCentre Hospitalier Universitaire de Sherbrooke
Fundersnot available
KeywordsPregnancyObesityMedicineGestational diabetesBody mass indexPsychosocialChildhood obesityObstetricsEnvironmental healthPrenatal careCaesarean sectionGestationOverweightEndocrinologyPsychiatryBiologyPopulation

Abstract

fetched live from OpenAlex

Childhood obesity is a predictor of adult obesity and has its roots in the pre-pregnancy or pregnancy period. This review presents an overview of the prenatal risk factors for childhood obesity, which were categorized into 2 groups: biological risk factors (maternal pre-pregnancy body mass index, gestational weight gain, diabetes in pregnancy, and caesarean section), and environmental and behavioural risk factors (maternal smoking and exposure to obesogens, maternal dietary patterns, maternal intestinal microbiome and antibiotics exposure, and maternal psychosocial stress). Identifying modifiable predisposing prenatal factors for obesity will inform further development of inventions to prevent obesity over the life course, and future directions for research and intervention are discussed.

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)0.001
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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.042
GPT teacher head0.359
Teacher spread0.318 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations34
Published2019
Admission routes2
Has abstractyes

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Same venueCanadian Journal of Physiology and PharmacologySame topicBirth, Development, and HealthFrench-language works237,207