ArticleScientific reports2022
Machine learning-based risk factor analysis of adverse birth outcomes in very low birth weight infants.
Article in Scientific reports, 2022. 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, 20 citations in OpenAlex.
- Predicting low birth weight in Bangladesh using interpretable machine learning models.BMC pregnancy and childbirth · 2026Article
- Machine learning-based prognostic modeling of early clinical outcomes in very low birth weight infants: insights from a nationwide cohort on delivery room resuscitation, prematurity-related complications, and mortality.BMC medical informatics and decision making · 2026Article
- Explainable Artificial Intelligence for Rehospitalization and Financial Burden of Fertile Women in Orthopedic Care.Healthcare (Basel, Switzerland) · 2026Article
- Low birth weight risk prediction model: a prognostic study in the Birhan field site in Ethiopia.Journal of global health · 2025Article
- Early prediction of mortality and morbidities in VLBW preterm neonates using machine learning.Pediatric research · 2025Article
- Utilization of tree-based machine learning models for predicting low birth weight cases.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
- Antenatal prediction of small for gestational age at birth based on four birthweight standards using machine learning algorithms.Frontiers in artificial intelligence · 2025Article
- Graph Machine Learning With Systematic Hyper-Parameter Selection on Hidden Networks and Mental Health Conditions in the Middle-Aged and Old.Psychiatry investigation · 2024Article
- A Predictive Model for Perinatal Brain Injury Using Machine Learning Based on Early Birth Data.Children (Basel, Switzerland) · 2024Article
- Explainable artificial intelligence on safe balance and its major determinants in stroke patients.Scientific reports · 2024Article
- Predicting adverse birth outcome among childbearing women in Sub-Saharan Africa: employing innovative machine learning techniques.BMC public health · 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
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aimed to analyze major predictors of adverse birth outcomes in very low birth weight (VLBW) infants including particulate matter concentration (PM
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.