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Background
Due to the highly variable clinical phenotype, Klinefelter Syndrome is underdiagnosed.
Objective
Assessment of supervised machine learning based prediction models for identification of Klinefelter Syndrome among azoospermic patients, and comparison to expert clinical evaluation.
Materials and methods
Retrospective patient data (karyotype, age, height, weight, testis volume, follicle‐stimulating...
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