Trial reportPloS one2023
A new method for customized fetal growth reference percentiles.
Trial report in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00912132 (The National Standard for Normal Fetal Growth), which is not on this map. Cited by 3 papers.
What it found
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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.
The National Standard for Normal Fetal Growth
Who cites it
3 citing papers in PubMed.
- The Fetal Region-specific Optimized Growth Standard (FROGS)-A fetal and birthweight centile calculator validated in a national population.PLoS medicine · 2025Article
- Low and High Birth Weight in a Hospital Population in Qassim, Saudi Arabia: An Analysis of Clinical Cutoff Values.Children (Basel, Switzerland) · 2024Article
- Machine learning models for estimating fetal weight based on ultrasonographic biometry: Development and validation study.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundCustomized fetal growth charts assume birthweight at term to be normally distributed across the population with a constant coefficient of variation at earlier gestational ages. Thus, standard deviation used for computing percentiles (e.g., 10th, 90th) is assumed to be proportional to the customized mean, although this assumption has never been formally tested.
methodsIn a secondary analysis of NICHD Fetal Growth Studies-Singletons (12 U.S. sites, 2009-2013) using longitudinal sonographic biometric data (n = 2288 pregnancies), we investigated the assumptions of normality and constant coefficient of variation by examining behavior of the mean and standard deviation, computed following the Gardosi method. We then created a more flexible model that customizes both mean and standard deviation using heteroscedastic regression and calculated customized percentiles directly using quantile regression, with an application in a separate study of 102, 012 deliveries, 37-41 weeks.
resultsAnalysis of term optimal birthweight challenged assumptions of proportionality and that values were normally distributed: at different mean birthweight values, standard deviation did not change linearly with mean birthweight and the percentile computed with the normality assumption deviated from empirical percentiles. Composite neonatal morbidity and mortality rates in relation to birthweight < 10th were higher for heteroscedastic and quantile models (10.3% and 10.0%, respectively) than the Gardosi model (7.2%), although prediction performance was similar among all three (c-statistic 0.52-0.53).
conclusionsOur findings question normality and constant coefficient of variation assumptions of the Gardosi customization method. A heteroscedastic model captures unstable variance in customization characteristics which may improve detection of abnormal growth percentiles.
trial registrationClinicalTrials.gov identifier: NCT00912132.
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Registered trials
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