Factors Affecting Turkish Medical Students' Pursuit of a Career in Neurosurgery: A Single Center Survey Study.

BACKGROUND: Statistics show that over the past 2 decades, even in high-income countries, fewer and fewer students have listed neurosurgery as their top career option. Literature on medical students' pursuit of neurosurgical careers in middle- and low-income countries are scarce. The aim of this research, conducted in Turkey with a middle-income economy, was to contribute insights relevant to medical education and neurosurgery across the world. METHODS: A survey was conducted with a target sample of fourth-year (167 students), fifth-year (169 students), and sixth-year (140 students) medical students (476 in total) from the Medical School at Istanbul Medeniyet University in Turkey. The response rates of the fourth-, fifth-, and sixth-year students were 62% (104/167), 53% (90/169), and 50% (70/140), respectively (in total, 266, including 147 female and 119 male). RESULTS: In terms of the genuine intention, only 2.5% of men and 2.7% of women were committed to specializing in neurosurgery. This study further revealed that possible reasons for these students' low motivation to specialize in neurosurgery were their beliefs that in neurosurgery, the physical and psychological demands were high, and the night shifts were intense, meaning they would not have a social life or spare time for their hobbies; that morbidity/mortality were high; and that financial incentives were insufficient, especially in public institutions. CONCLUSION: Turkish medical students did not rank neurosurgery at the top of their career choices. Possible reasons for this are socioeconomic factors and the inadequate introduction of neurosurgery to medical students.

The impact of menopause and serum DHEA-S level on the severity of Covid-19.

OBJECTIVES: We aimed to demonstrate the difference between premenopausal and postmenopausal women in respect of the clinical course and outcomes of Covid-19. We investigated the epidemiological and hormonal factors which influence the severity of the disease. STUDY DESIGN: This observational cross-sectional study included the female patients admitted to a Covid-19 outpatient clinic between July 2020 and June 2021 and diagnosed with a positive polymerase chain reaction test. Blood samples were obtained to determine the serum levels of follicle stimulating hormone, luteinizing hormone, estradiol, total testosterone and dehydroepiandrosterone sulfate. MAIN OUTCOME MEASURE: Our primary outcome was the poor clinical course of the disease in postmenopausal women. Our secondary outcome was the contribution of hormonal status to the clinical course of the disease. RESULTS: Our cohort consisted of 253 female patients (85 with mild, 125 with moderate and 43 with severe disease; 101 at the premenopausal and 152 at the postmenopausal stage). There was a statistically significant difference between the patients in different severity groups regarding clinical data and serum levels of luteinizing hormone, follicle stimulating hormone, estradiol and dehydroepiandrosterone sulfate. Being one year younger decreased the odds of having severe Covid-19 0.338-fold relative to the group with mild disease. A decrease in the serum dehydroepiandrosterone sulfate level was associated with a 2.604-fold increase in the odds of having severe Covid-19 relative to the group with mild disease. Being postmenopausal increased the odds of having severe disease compared with mild disease by 2.687-fold. CONCLUSIONS: The prognosis of Covid-19 is more favorable in premenopausal women compared with postmenopausal women. Age, postmenopausal status and serum levels of dehydroepiandrosterone sulfate are important predictors of the severity of Covid-19 for women.

ChatGPT-assisted deep learning model for thyroid nodule analysis: beyond artifical intelligence.

AIMS: To develop a deep learning model, with the aid of ChatGPT, for thyroid nodules, utilizing ultrasound images. The cytopathology of the fine needle aspiration biopsy (FNAB) serves as the baseline. MATERIAL AND METHODS: After securing IRB approval, a retrospective study was conducted, analyzing thyroid ultrasound images and FNAB results from 1,061 patients between January 2017 and January 2022. Detailed examinations of their demographic profiles, imaging characteristics, and cytological features were conducted. The images were used for training a deep learning model to identify various thyroid pathologies. ChatGPT assisted in developing this model by aiding in code writing, preprocessing, model optimization, and troubleshooting. RESULTS: The model demonstrated an accuracy of 0.81 on the testing set, within a 95% confidence interval of 0.76 to 0.87. It presented remarkable results across thyroid subgroups, particularly in the benign category, with high precision (0.78) and recall (0.96), yielding a balanced F1-score of 0.86. The malignant category also displayed high precision (0.82) and recall (0.92), with an F1-score of 0.87. CONCLUSIONS: The study demonstrates the potential of artificial intelligence, particularly ChatGPT, in aiding the creation of robust deep learning models for medical image analysis.

Associations between sleep characteristics and glycemic variability in youth with type 1 diabetes.

OBJECTIVE: This study aimed to determine sleep characteristics and their associations with glycemic variability in youth with type 1 diabetes (T1D). MATERIAL AND METHODS: This cross-sectional study conducted at two pediatric diabetes centers in Istanbul, Turkey, included 84 children with T1D (mean age 10.5 years). Sleep characteristics and glycemic variability were determined by actigraphy, DSM-5 Level 2-Sleep Disturbance Scale Short Form and continuous glucose monitoring. Circadian preference was evaluated by the Children's Chronotype Questionnaire. Sleep disturbances were assessed by the. The sleep quality was determined by actigraphy-derived sleep measures. RESULTS: Eighty-eight percent of participants had insufficient age-appropriate total sleep time (TST) (<9 h for 6-13-year-olds and <8 h for 14-17-year-olds). Chronotype was classified as intermediate in 50%, evening in 45.2%, and morning in 4.8%. A higher chronotype score indicating a stronger eveningness preference was associated with more time spent in hypoglycemia (ß = 0.433, p = 0.002). On nights when participants had lower sleep efficiency and longer sleep onset latency, they had significantly higher overnight glycemic variability (ß = -0.343, p = 0.016, ß = 0.129, p = 0.017, respectively). Prolonged nocturnal wake duration was significantly associated with more time spent in daytime hypoglycemia (ß = 0.037, p = 0.046) and higher overnight glycemic variability (J index, ß = 0.300, p = 0.015). The associations between TST and glycemic variability indices were not significant. CONCLUSIONS: Sleep quality rather than TST was significantly associated with glycemic variability in children with T1D. Eveningness preference might contribute to an increased risk of hypoglycemia. Addressing sleep patterns and chronotypes can be crucial in management plans for youth with T1D.