Publications by authors named "M Kaynar"
Article Synopsis
- The study explores how different sexual positions impact ejaculation duration and the overall control of ejaculation, which is important for sexual pleasure.
- Conducted through an online survey with 1904 heterosexual men aged 18-65, it looked at factors related to premature ejaculation (PE) and how these men engage in various sexual positions.
- Results indicated a significant preference for doggy style across all groups, with non-PE participants more likely to change positions when close to ejaculation compared to PE participants, suggesting that adjusting sexual positions may help manage PE.
Introduction: The objective of this study is to predict the probability of prostate cancer in PI-RADS 3 lesions using machine learning methods that incorporate clinical and mpMRI parameters.
Methods: The study included patients who had PI-RADS 3 lesions detected on mpMRI and underwent fusion biopsy between January 2020 and January 2024. Radiological parameters (Apparent diffusion coefficient (ADC), tumour ADC/contralateral ADC ratio, Ktrans value, periprostatic adipose tissue thickness, lesion size, prostate volume) and clinical parameters (age, body mass index, total prostate specific antigen, free PSA, PSA density, systemic inflammatory index, neutrophil-lymphocyte ratio [NLR], platelet lymphocyte ratio, lymphocyte monocyte ratio) were documented.
Objectives: Artificial intelligence (AI) applications are increasingly being utilized by both patients and physicians for accessing medical information. This study focused on the urolithiasis section (pertaining to kidney and ureteral stones) of the European Association of Urology (EAU) guideline, a key reference for urologists.
Material And Methods: We directed inquiries to four distinct AI chatbots to assess their responses in relation to guideline adherence.
Purpose: To predict the post transurethral prostate resection(TURP) urethral stricture probability by applying different machine learning algorithms using the data obtained from preoperative blood parameters.
Methods: A retrospective analysis of data from patients who underwent bipolar-TURP encompassing patient characteristics, preoperative routine blood test outcomes, and post-surgery uroflowmetry were used to develop and educate machine learning models. Various metrics, such as F1 score, model accuracy, negative predictive value, positive predictive value, sensitivity, specificity, Youden Index, ROC AUC value, and confidence interval for each model, were used to assess the predictive performance of machine learning models for urethral stricture development.
Background: Although patients are commonly monitored for depth of anesthesia, it is unclear to what extent administration of intravenous anesthetic medications may affect calculated bispectral (BIS) index values under general anesthesia.
Methods: In a retrospective analysis of electronic anesthesia records from an academic medical center, we examined BIS index changes associated with 14 different intravenous medications, as administered in routine practice, during volatile-based anesthesia using a novel screening approach. Discrete-time windows were identified in which only a single drug bolus was administered, and subsequent changes in the BIS index, concentration of volatile anesthetic, and arterial pressure were analyzed.