{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T02:00:30Z","timestamp":1785463230581,"version":"3.56.0"},"reference-count":45,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T00:00:00Z","timestamp":1766361600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T00:00:00Z","timestamp":1766361600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["obgyn.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Intl J Gynecology &amp;amp; Obste"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study determines whether a machine\u2010learning model integrating sonographic biometry with maternal clinical parameters improves prediction of large\u2010for\u2010gestational\u2010age (LGA) compared with Hadlock's EFW formula.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a retrospective cohort study including all singleton live births at \u226532 gestational weeks at a tertiary medical center. Predictors comprised biparietal diameter, abdominal circumference, femur length, maternal demographics and anthropometrics, obstetric history, chronic and gestational morbidity, and glucose values from screening and diagnosis. A CatBoost gradient\u2010boosting model estimated the probability of LGA (birthweight \u226590th percentile). Performance was compared with Hadlock's EFW using area under the curve (AUC) and detection at a fixed 10% false\u2010positive rate. A prespecified subgroup analysis evaluated pregnancies with pregestational or gestational diabetes. Performance was assessed with fivefold cross\u2010validation; calibration and utility were examined by decision curve analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Among 31\u2009531 parturients, 18.17% delivered an LGA neonate. The model achieved an AUC of 0.946 (95% confidence interval [CI], 0.938\u20130.955), significantly outperforming Hadlock's EFW (AUC 0.867; 95% CI, 0.854\u20130.881;\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.01) and yielding a higher detection rate at a 10% false\u2010positive rate (79% vs. 63%). The most influential contributors were abdominal circumference, gestational age at delivery, fetal sex, and maternal age. In 3871 diabetic pregnancies, among whom 24% delivered LGA, performance remained high (AUC 0.890; 95% CI, 0.847\u20130.918) and exceeded Hadlock's formula (AUC 0.820; 95% CI, 0.772\u20130.863;\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.02).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>A predictive algorithm, incorporating sonographic and non\u2010sonographic features, as developed here, achieved superior accuracy compared to the traditional EFW formula in predicting LGA neonates, in both general and diabetic pregnant populations.<\/jats:p>\n                  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