Background: Colorectal cancer (CRC) is the second most prevalent malignancy in Jordan. Because early detection can greatly improve treatment outcomes, it is crucial to increase awareness of signs and symptoms, risk factors, and the significance of routine CRC screenings. In this study, we aimed to assess awareness levels regarding CRC symptoms and risk factors among adults in Jordan and to identify barriers to CRC screening. Methods: This web-based cross-sectional study was conducted in Jordan from March 5, 2024 to July 9, 2024, and targeted people aged 50–75 years who had no history of CRC. The sample size was calculated via a convenience sampling method. Data were collected via a validated, culturally adapted survey. Descriptive analysis was used when appropriate. Analytic statistics were performed to predict participants’ awareness of CRC symptoms and risk factors. Results: The study included 400 participants, with a mean age of 58.42 years (SD = 6.511). More than half of the respondents were females (56.5%). The mean awareness score of CRC symptoms among the study participants was 4.97/9 (SD = 1.18), whereas that of risk factors was 5.21/10 (SD = 1.53). The overall mean awareness score was 10.18/19 (SD = 2.65). The top three reported barriers to CRC screening were: not at risk due to absence of symptoms (61.8%), not at risk due to adopting a healthy lifestyle (56.8%), not at risk due to absence of family history (51.8%). Conclusion: Colorectal cancer awareness among the population was relatively low, with significant symptoms and risk factors being overlooked by the participants. In addition to that, notable barriers to screening, especially fear and embarrassment of the screening test, have surfaced. This prompts the need for more cancer education and healthcare provider involvement to overcome screening barriers and promote participation in screening programs to enable early detection.

Laparoscopic adrenalectomy has become the standard approach for most benign adrenal lesions. However, the number of cases required for a surgeon to achieve

This study aimed to introduce a novel machine learning (ML) model designed to predict the need of interventions during endoscopy in patients with upper gastrointestinal bleeding (UGIB). The risk stratification tools in current use, such as the Glasgow Blatchford Score (GBS) and pre-endoscopic Rockall score, have limitations in accurately predicting the need for endoscopic interventions. All patients diagnosed with UGIB from January 2013 to October 2023 who underwent endoscopy were included in the study. Variables extracted included demographics, social history ,clinical history, clinical presentation and symptoms, drug history, management prior to endoscopy, findings during endoscopy, laboratory variables and vitals, and post-endoscopic results. Three machine learning models including Random Forest Classifier (RFC), Gradient Boosting Classifier (GBC), and AdaBoost Classifier were used to evaluate the need for intervention during endoscopy in our study. A total of 1372 patients were included from three major hospitals in Jordan, of whom 242 underwent endoscopic intervention. The GBC outperformed all other models and pre-endoscopic scores in predicting the need for endoscopic intervention with an area under the curve of 0.861. Findings during endoscopy, platelet count, pulse rate and systolic blood pressure during admission were the most contributing features in predicting the need for the need of endoscopic intervention. This study highlights the potential of machine learning models in enhancing decision making for UGIB management.