The early prediction of gestational diabetes mellitus by machine learning models.

Background: We aimed to determine the best-performing machine learning (ML)-based algorithm for predicting gestational diabetes mellitus (GDM) with sociodemographic and obstetrics features in the pre-conceptional period.

Methods: We collected the data of pregnant women who were admitted to the obstetric clinic in the first trimester. The maternal age, body mass index, gravida, parity, previous birth weight, smoking status, the first-visit venous plasma glucose level, the family history of diabetes mellitus, and the results of an oral glucose tolerance test of the patients were evaluated.

Examination of the effect of COVID-19 on sexual dysfunction in women.

Objective: To investigate the effect of COVID-19 on sexual dysfunction in women.

Material And Methods: The women diagnosed with COVID-19 and hospitalised at a tertiary hospital were included. They completed the Introductory Data Form, the Female Sexual Function Index-(FSFI) and the Short Form-36 Quality of Life Scale (SF-36).

Could LUTS be early symptoms of COVID-19.

Aim: To show if lower urinary tract symptoms (LUTS) could be symptoms of COVID-19 with validated questionnaires.

Methods: The 96 COVID-19 patients who were hospitalised at a tertiary centre were collected retrospectively. After the exclusion criteria, 46 patients consisted the study population.