Related Experiment Video
Updated: Oct 29, 2025

09:36
Assessment of Child Anthropometry in a Large Epidemiologic Study
Published on: February 2, 2017
27.4K
Customized compared to population-based centiles for detecting term small for gestational age infants in Greece
D Rallis1, P Karagianni1, E Papaharalambous1
1Second Neonatal Intensive Care Unit and Neonatology Department of Aristotle University of Thessaloniki, School of Medicine, Thessaloniki, Greece.
Hippokratia
|July 9, 2021
Summary
Customized centiles more accurately identify small for gestational age (SGA) infants compared to population-based centiles. However, neither method significantly improved prediction of poor perinatal outcomes in this Greek cohort.
Area of Science:
- Neonatalogy
- Perinatal Medicine
- Biostatistics
Background:
- Accurate detection of small for gestational age (SGA) infants is crucial for identifying those at risk of morbidity.
- The effectiveness of customized centiles versus population-based centiles in predicting adverse perinatal outcomes remains unclear.
- This study investigates the validity of customized centiles in a Greek cohort for SGA detection and morbidity risk prediction.
Purpose of the Study:
- To evaluate the accuracy of customized centiles in identifying SGA term infants within a Greek population.
- To compare the performance of customized centiles against population-based centiles in predicting perinatal morbidity.
- To assess the clinical utility of adjusted centiles for risk stratification in newborns.
Main Methods:
- Prospective data collection of neonatal and maternal characteristics for singleton, low-risk, term infants over one year.
- Infants classified as SGA based on birth weight below the tenth centile using both population-based and customized centiles (adjusted for maternal/innate factors).
- Comparative analysis using linear regression and receiver operating characteristic (ROC) curves to assess predictive performance.
Main Results:
- Customized centiles identified a higher proportion of SGA infants (12%) compared to population-based centiles (6%).
- Both customized and population-based centiles showed a similar association with perinatal morbidity (OR 1.02).
- No significant difference was observed in predicting perinatal morbidity between customized and population-based centiles (AUC 0.773 vs. 0.737, p=0.272).
Conclusions:
- Customized centiles demonstrate superior accuracy in detecting SGA term infants compared to population-based centiles.
- Despite improved SGA detection, customized centiles did not enhance the prediction of poor perinatal outcomes.
- The findings suggest that while customized centiles improve SGA identification, their role in predicting morbidity requires further investigation.
Related Concept Videos
Regression Toward the Mean
6.6K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.6K
Testing a Claim about Standard Deviation
2.6K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
2.6K
z Scores and Area Under the Curve
15.3K
z scores are the standardized values obtained after converting a normal distribution into a standard normal distribution. A z score is measured in units of the standard deviation. The z score tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a z score of...
15.3K

