Small for gestational age

Small for gestational age (SGA) newborns are those who are smaller in size than normal for the gestational age. SGA is most commonly defined as a weight below the 10th percentile for the gestational age. SGA predicts susceptibility to hypoglycemia, hypothermia, and polycythemia. By definition, at least 10% of all newborns will be labeled SGA. All SGA babies should be watched for signs of failure to thrive, hypoglycemia and other health conditions. Being small for gestational age is broadly either: Being constitutionally small, or caused by a genetic trait of the baby Intrauterine growth restriction, also called "pathological SGA" The condition is defined by birth weight and/or length. Intrauterine growth restriction is generally diagnosed by measuring the mother's uterus, with the fundal height being less than it should be for that stage of the pregnancy. If it is suspected, the mother will usually be sent for an ultrasound to confirm. Ninety percent of babies born SGA catch up in growth by the time they reach 2 years old. For the 10 percent of those that are SGA without catchup growth by 2 years old, an endocrinologist should be consulted. Some cases warrant growth hormone therapy. Hypoglycemia is common in asymmetrical SGA babies because their larger brains burn calories at a faster rate than their usually limited fat stores hold . Hypoglycemia is treated by frequent feedings and/or additions of cornstarch-based products (such as Duocal powder) to the feedings. There are some common conditions and disorders found in many that are SGA (and especially those that are SGA without catchup growth by 2 years old). Gastroenterologist - for gastrointestinal issues such as reflux and/or delayed gastric emptying Dietitian - to address caloric deficits. Dietitians are usually brought in for cases that include failure to thrive. According to the theory of thrifty phenotype, causes of growth restriction also trigger epigenetic responses in the fetus that are otherwise activated in times of chronic food shortage, and if the offspring develops in an environment rich in food, it may be more prone to metabolic disorders such as obesity and type II diabetes.

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related concepts (16)

Related lectures (4)

Small for gestational age

Pregnancy

Pregnancy is the time during which one or more offspring develops (gestates) inside a woman's uterus (womb). A multiple pregnancy involves more than one offspring, such as with twins. Pregnancy usually occurs by sexual intercourse, but can also occur through assisted reproductive technology procedures. A pregnancy may end in a live birth, a miscarriage, an induced abortion, or a stillbirth. Childbirth typically occurs around 40 weeks from the start of the last menstrual period (LMP), a span known as the gestational age.

Fetus

A fetus or foetus (ˈfiːtəs; : fetuses, feti, foetuses, or foeti) is the unborn offspring that develops from an animal embryo. Following embryonic development the fetal stage of development takes place. In human prenatal development, fetal development begins from the ninth week after fertilization (or eleventh week gestational age) and continues until birth. Prenatal development is a continuum, with no clear defining feature distinguishing an embryo from a fetus.

Causal Inference in Epidemiology: Pregnancy and Prostate Cancer

Explores causal inference in epidemiology, focusing on COVID-19's impact on preterm birth and refining treatment strategies for prostate cancer.

Linear Regression Basics CS-233(a): Introduction to machine learning (BA3)

Covers the basics of linear regression in machine learning, including model training, loss functions, and evaluation metrics.

Linear Regression: Basics and Applications DH-406: Machine learning for DH

Covers the basics of linear regression in machine learning, exploring its applications in predicting outcomes like birth weight and analyzing relationships between variables.