ArticleJournal of perinatology : official journal of the California Perinatal Association2023
Developing a resiliency model for survival without major morbidity in preterm infants.
Article in Journal of perinatology : official journal of the California Perinatal Association, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed, 3 citations in OpenAlex.
- "Actionable" risk for preterm birth: patterns and prediction in California singleton births 2016-2020.BMC pregnancy and childbirth · 2026Article
- Quantitative assessment of neonatal health using dried blood spot metabolite profiles and deep learning.Science translational medicine · 2026Article
- Survival without major morbidity in very preterm infants (<32 weeks' gestation): prevalence, temporal trends, and associated factors in a single-center retrospective cohort.Frontiers in pediatrics · 2026Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveDevelop and validate a resiliency score to predict survival and survival without neonatal morbidity in preterm neonates <32 weeks of gestation using machine learning. STUDY
designModels using maternal, perinatal, and neonatal variables were developed using LASSO method in a population based Californian administrative dataset. Outcomes were survival and survival without severe neonatal morbidity. Discrimination was assessed in the derivation and an external dataset from a tertiary care center.
resultsDiscrimination in the internal validation dataset was excellent with a c-statistic of 0.895 (95% CI 0.882-0.908) for survival and 0.867 (95% CI 0.857-0.877) for survival without severe neonatal morbidity, respectively. Discrimination remained high in the external validation dataset (c-statistic 0.817, CI 0.741-0.893 and 0.804, CI 0.770-0.837, respectively).
conclusionOur successfully predicts survival and survival without major morbidity in preterm babies born at <32 weeks. This score can be used to adjust for multiple variables across administrative datasets.
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.