ReviewChildhood obesity (Print)2021
Identifying Key Determinants of Childhood Obesity: A Narrative Review of Machine Learning Studies.
Review in Childhood obesity (Print), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it, 47 citations in OpenAlex.
- Identifying Key Predictors of Mid-Childhood Obesity in a Population-Based Cohort Study: An Evidence Synthesis and Predictive Modeling Study.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2025Pooled it
- Systematic Review of Machine Learning applied to the Prediction of Obesity and Overweight.Journal of medical systems · 2023Pooled it
- A prediction model for childhood obesity risk based on maternal thyroid status and related parameters using machine learning: a mother-newborn-offspring study in a mild-to-moderate iodine deficiency area.International journal of obesity (2005) · 2026Article
- Predicting obesity at adolescence from an early age in a Dutch observational cohort study: the development and internal validation of a multivariable prediction model.BMC pediatrics · 2025Observational
- Exploring the multifaceted factors influencing overweight and obesity: a scoping review.Frontiers in public health · 2025Article
- FACILITY: feeding the family-the intergenerational approach to fight obesity, a cross-sectional study protocol.Frontiers in pediatrics · 2025Article
- SHAP-enhanced machine learning identifies modifiable obesity predictors across adolescent weight groups: A 2021 YRBSS analysis.PloS one · 2025Article
- Prevalence of obesity and associated sociodemographic and lifestyle factors in Ecuadorian children and adolescents.Pediatric research · 2025Article
- Validation of Variables for Use in Pediatric Obesity Risk Score Development in Demographically and Racially Diverse United States Cohorts.The Journal of pediatrics · 2024Article
- Prediction of adolescent weight status by machine learning: a population-based study.BMC public health · 2024Article
- Article
- Physical activity on prescription for children with obesity: a focus group study exploring experiences in paediatric healthcare.Frontiers in health services · 2024Article
- Prediction and classification of obesity risk based on a hybrid metaheuristic machine learning approach.Frontiers in big data · 2024Article
- Predicting body mass index in early childhood using data from the first 1000 days.Scientific reports · 2023Article
- Predicting Childhood Obesity Based on Single and Multiple Well-Child Visit Data Using Machine Learning Classifiers.Sensors (Basel, Switzerland) · 2023Article
- Predicting risk of obesity in overweight adults using interpretable machine learning algorithms.Frontiers in endocrinology · 2023Article
- Parental determinants associated with early growth after the first year of life by race and ethnicity.Frontiers in pediatrics · 2023Article
- Machine Learning in Nutrition Research.Advances in nutrition (Bethesda, Md.) · 2022Article
- Machine learning insights concerning inflammatory and liver-related risk comorbidities in non-communicable and viral diseases.World journal of gastroenterology · 2022Review
- Implementation of physical activity on prescription for children with obesity in paediatric health care (IMPA): protocol for a feasibility and evaluation study using quantitative and qualitative methods.Pilot and feasibility studies · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Machine learning is a class of algorithms able to handle a large number of predictors with potentially nonlinear relationships. By applying machine learning to obesity, researchers can examine how risk factors across multiple settings (
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.