Diagnosis of Polycystic Ovary Syndrome With Predictive Modeling of Select Clinical Features
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The authors aimed to determine if a limited set of ultrasonographic, biochemical, and clinical features could accurately predict a diagnosis of polycystic ovary syndrome (PCOS). Their findings revealed that a model incorporating anti-müllerian hormone (AMH) and ovarian volume demonstrated high diagnostic accuracy, suggesting that a streamlined approach using fewer variables could effectively diagnose PCOS while reducing clinical burden.
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