Backgrounds. Deep Transcranial Magnetic Stimulation (dTMS) is a non-invasive treatment cleared by FDA as a safe and efficient intervention for the treatment of depression and obsessive-compulsive disorder (OCD). Objectives. In this retrospective single-center study, the effects of dTMS on the electrophysiological parameters and the clinical outcomes of patients with OCD were tested. Methods. Thirty sessions of dTMS were administered to 29 OCD patients (15 female and 14 male). Quantitative electroencephalography (QEEG) recordings and Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) were measured at baseline and endpoint. Paired sample t-test was used to measure the change in Y-BOCS scores and QEEG activity after dTMS practice. Results. All 29 patients responded to the dTMS intervention by indicating at least 35% reduction in Y-BOCS scores. QEEG recordings revealed a significant decrease in theta, alpha and the beta rhythms. The decrease in the severity of OCD symptoms correlated with the decrease in beta activity at left central region. Conclusions. Historically, excess fast oscillations in OCD are correlated with the unresponsiveness to selective serotonin reuptake inhibitor (SSRI) treatment. We hypothesize that the decrease in the power of beta bands by deep TMS is related to the mechanism of the therapeutic response.

Background: The aim of this study is to evaluate the hypothesis test results after categorizing the scale scores with cut-off points and to assess whether similar results would be obtained in that best represent the categories. Methods: This cross-sectional study was conducted between March 15 and 20, 2023 via the Lime Survey. The questionnaire included questions about the sociodemographic and life characteristics of the participants and the Beck Depression Inventory II (BDI-II). Four groups (minimal, mild, moderate, severe depression) were formed using the cutoff points. Data analysis was performed with all participants and referred to as the conventional analysis group. Then, six subanalysis groups were determined to best represent the groups formed according to the BDI-II. In each BDI-II category, six subanalysis groups were created, including those between Q1–Q3 (IQR group), including those within ± 1 std, including those between 5p–95p (90% of the sample), including those between 2.5p–97.5p (95% of the sample). In addition, 100 different samples were randomly selected containing 50% of each group. Results: Of the 1950 participants, 84.7% (n = 1652) were female and 15.3% (n = 298) were male. In terms of depression, it was observed that the significance varied in the analysis groups for sex (p = 0.039), medication use (p = 0.009) and age (p = 0.010) variables. However, these variables were not significant in some of the subanalysis groups. On the other hand, a p < 0.001 value was obtained for income, physical activity, health perception, body shape perception, life satisfaction, and quality of life variables in terms of depression in the conventional analysis group, and it was seen that the significance continued in all subanalysis groups. Conclusions: Our findings showed that variables with p < 0.001 in the conventional analysis group maintained their significance in the other analysis groups. In addition, as the p value got closer to 0.05, we observed that the significance changed according to different cutoff points in the analysis groups. In addition, 50% randomly selected samples support these results. At the end of our study, we reached results that support the necessity of secondary tests in the evaluation of scales. Although further studies are needed, we anticipate that our study will shed light on other studies.

This study aims to propose and evaluate a modified version of the One-Sample Kolmogorov-Smirnov (K-S) test that addresses its current limitations in large sample groups, with the goal of improving its accuracy and reliability in assessing normality assumptions in medical research data. In addition to the classical K-S test, a logarithmic modification was applied to reduce the impact of sample size. This modification replaces the sample size in the test calculation with a logarithmic formula (ln n²) to prevent z-values from becoming excessively small in large samples. Statistical analyses were conducted using Microsoft 365/Excel, SPSS 21.0 and STATA/MP18 with a geometric approach employed to assess data normality using the Geometric Approach to Normality Testing. The study analyzed real-world laboratory data obtained from the complete blood count (CBC) results of 122,310 adult patients (aged ≥18 years) who were treated at Cerrahpaşa Medical Faculty Hospital throughout 2022. The modified K-S test with the proposed logarithmic modification (ln n²) reduced the tendency to reject normality solely due to large sample size. The modified test was able to confirm that some hematological parameters did indeed fit normal distribution models, while discriminating those that did not. In particular, analysis of the data set trimmed by 0.5% showed further improvement in test performance. Consequently, the proposed modification is shown to provide a more sensitive method for assessing the assumption of normal distribution in large data sets. The method can be easily integrated into existing statistical software, making it accessible for routine use in large-scale data analysis.

Coronavirus disease 2019 (COVID-19), caused by the SARS-CoV-2 virus, typically presents with mild to moderate course in the majority of infected individuals. During the pandemic, nearly 30% of patients required hospitalization, and a subset developed a severe disease characterized by respiratory failure and needed intensive care support.