ArticleBMC pregnancy and childbirth2023
Building a predictive model of low birth weight in low- and middle-income countries: a prospective cohort study.
Article in BMC pregnancy and childbirth, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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
12 citing papers in PubMed.
- Multiple Output Gaussian Process Model for Predicting Low Birth Weight in Medellín, Colombia: An Alternative to Conventional Machine Learning Models.Maternal and child health journal · 2026Article
- Predicting abnormal birth weight and identifying associated factors using machine learning in the Hararghe Health and Demographic Surveillance System, Ethiopia.BMC public health · 2026Article
- Deep learning-based neonatal outcome prediction: an LSTM autoencoder framework for pattern analysis and risk assessment.Scientific reports · 2026Article
- Fairness in Low Birthweight Predictive Models: Implications of Excluding Race/Ethnicity.Journal of racial and ethnic health disparities · 2026Article
- Article
- Interpretable clinical decision support systems in high-risk pregnancy: a scoping review of models, methods, and implementation.BMC pregnancy and childbirth · 2026Article
- Low birth weight risk prediction model: a prognostic study in the Birhan field site in Ethiopia.Journal of global health · 2025Article
- Predictive models for low birth weight: a comparative analysis of algorithmic fairness-improving approaches.The American journal of managed care · 2025Article
- Predicting low birth weight risks in pregnant women in Brazil using machine learning algorithms: data from the Araraquara cohort study.BMC pregnancy and childbirth · 2025Article
- Causal machine learning models for predicting low birth weight in midwife-led continuity care intervention in North Shoa Zone, Ethiopia.BMC medical informatics and decision making · 2025Article
- Prediction of low birth weight from fetal ultrasound and clinical characteristics: a comparative study between a low- and middle-income and a high-income country.BMJ global health · 2024Article
- Predictive value of Vitamin D levels in pregnant women on gestational length and neonatal weight in China: a population-based retrospective study.Reproductive biology and endocrinology : RB&E · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
24 authors.
Funding
Abstract
backgroundLow birth weight (LBW, < 2500 g) infants are at significant risk for death and disability. Improving outcomes for LBW infants requires access to advanced neonatal care, which is a limited resource in low- and middle-income countries (LMICs). Predictive modeling might be useful in LMICs to identify mothers at high-risk of delivering a LBW infant to facilitate referral to centers capable of treating these infants.
methodsWe developed predictive models for LBW using the NICHD Global Network for Women's and Children's Health Research Maternal and Newborn Health Registry. This registry enrolled pregnant women from research sites in the Democratic Republic of the Congo, Zambia, Kenya, Guatemala, India (2 sites: Belagavi, Nagpur), Pakistan, and Bangladesh between January 2017 - December 2020. We tested five predictive models: decision tree, random forest, logistic regression, K-nearest neighbor and support vector machine.
resultsWe report a rate of LBW of 13.8% among the eight Global Network sites from 2017-2020, with a range of 3.8% (Kenya) and approximately 20% (in each Asian site). Of the five models tested, the logistic regression model performed best with an area under the curve of 0.72, an accuracy of 61% and a recall of 72%. All of the top performing models identified clinical site, maternal weight, hypertensive disorders, severe antepartum hemorrhage and antenatal care as key variables in predicting LBW.
conclusionsPredictive modeling can identify women at high risk for delivering a LBW infant with good sensitivity using clinical variables available prior to delivery in LMICs. Such modeling is the first step in the development of a clinical decision support tool to assist providers in decision-making regarding referral of these women prior to delivery. Consistent referral of women at high-risk for delivering a LBW infant could have extensive public health consequences in LMICs by directing limited resources for advanced neonatal care to the infants at highest risk.
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.