Background: Recent studies indicate that vitamin D impacts male reproductive function, with deficiency linked to infertility. This review evaluates the effect of vitamin D supplementation on male fertility, focusing on total testosterone, free testosterone, the free androgen index (FAI), follicle-stimulating hormone (FSH), luteinizing hormone (LH), sex-hormone-binding globulin (SHBG), and estradiol. Methods: We systematically searched Medline, Web of Science, Cochrane Library, and Scopus from their inception until July 2024 for randomized controlled trials (RCTs) involving adult males. The primary focus of these studies was on reproductive hormone parameters, analyzed using a random-effects meta-analysis and weighted mean difference (WMD). Evidence quality was assessed using ROB2 and GRADE. Meta-regression and dose–response analyses were performed. Results: Seventeen studies met the criteria for quantitative analysis. Vitamin D supplementation significantly increased total testosterone levels (WMD 0.38, 95% CI 0.06–0.70, n = 15, I² = 67.03). However, it had no significant effect on other hormone parameters: free testosterone (WMD 0.00, 95% CI −0.02–0.03, n = 9, I² = 48.12), FSH (WMD −0.02, 95% CI −0.57–0.53, n = 7, I² = 48.72), LH (WMD −0.09, 95% CI −0.30–0.12, n = 8, I² = 0.00), SHBG (WMD 0.73, 95% CI −1.14–2.61, n = 10, I² = 69.05), FAI (WMD −0.92, 95% CI −2.12–0.27, n = 6, I² = 0.00), and estradiol (WMD −0.02, 95% CI −2.95–2.92, n = 5, I² = 20.63). Conclusion: This meta-analysis shows that vitamin D supplementation may increase total testosterone levels in men. However, further well-designed RCTs are needed to determine vitamin D’s effects on other reproductive hormone parameters.

Background and Objectives: Distal radius fractures are among the most common pediatric injuries, accounting for approximately 25% of all fractures in children. Displaced fractures are prone to re-displacement, necessitating additional interventions. K-wire fixation is effective in reducing re-displacement risks, but no one has systematically assessed its use. This study aims to compare the outcomes of cast immobilization alone versus cast immobilization with K-wire fixation in pediatric patients with displaced distal radius fractures. Methods: A comprehensive search of PubMed, Web of Science, Cochrane CENTRAL, Scopus, and Embase databases for studies comparing these treatments. The quality assessment was conducted using the Cochrane Collaboration Risk of Bias for randomized studies and the Methodological Index for non-randomized studies. The meta-analysis was carried out using RevMan software V5.4. Results: Out of 267 initial records, 12 studies met the inclusion criteria, encompassing 1455 patients (853 treated with cast alone and 602 with K-wire fixation). Meta-analysis of 10 studies showed significantly higher re-displacement rates with cast immobilization compared to K-wire fixation (OR: 11.42, 95% CI: 2.43–53.77, p = 0.002, I² = 82%). The risk of secondary surgery was also higher in the cast group (OR: 6.91, 95% CI: 1.5–31.72, p = 0.01, I² = 75%). However, complications were lower with cast immobilization (OR: 0.68, 95% CI: 0.45–1.03, p = 0.07, I² = 74%), though not statistically significant. Conclusions: K-wire fixation appears to offer superior fracture stability and reduces the need for secondary surgeries compared to cast immobilization for displaced distal radius fractures in pediatric patients. However, both treatment modalities are associated with comparable complication rates, emphasizing the importance of individualized treatment planning.

Background: Osteosarcoma (OSC) is a rare but aggressive bone cancer and predicting survival outcomes remains a critical challenge in clinical practice. This study aims to evaluate the performance of machine learning models in predicting survival outcomes for OSC patients using regression and classification approaches. Methods: We analyzed data from 1471 OSC patients, with 55.7% male and a mean age of 26.13 years. Descriptive and survival analyses were performed using Python. Regression models (Linear Regression, LightGBM, XGBoost, and Random Forest) were deployed to predict survival as a continuous outcome and evaluated using mean absolute error (MAE), mean squared error (MSE), and R -squared ( R ² ). Classification models (Logistic Regression, Decision Tree, Support Vector Machine, and Random Forest) were used to predict 1-, 3-, and 5-year survival status, evaluated using accuracy, precision, recall, and F1 score. Results: Among regression models, XGBoost performed best, achieving the lowest MAE (3.715) and MSE (20.788) and the highest R ² (0.213). For classification tasks, Logistic Regression outperformed other models, achieving the highest accuracy for 1-year (0.829), 3-year (0.749), and 5-year (0.719) survival predictions. The Random Forest and Decision Tree models showed competitive performance, while the Support Vector Machine struggled with long-term survival predictions. Conclusion: Machine learning models, particularly XGBoost for regression and Logistic Regression for classification, demonstrate strong potential for predicting survival outcomes in OSC patients. These findings underscore the utility of machine learning in enhancing clinical decision-making and suggest avenues for future research, including incorporating additional clinical variables and advanced modeling techniques to improve long-term survival predictions.