Predicting the final grade using a machine learning regression model: insights from fifty percent of total course grades in CS1 courses.

This article introduces a model for accurately predicting students' final grades in the CS1 course by utilizing their grades from the first half of the course. The methodology includes three phases: training, testing, and validation, employing four regression algorithms: AdaBoost, Random Forest, Support Vector Regression (SVR), and XGBoost. Notably, the SVR algorithm outperformed the others, achieving an impressive R-squared () value ranging from 72% to 91%.

Early prediction of student performance in CS1 programming courses.

There is a high failure rate and low academic performance observed in programming courses. To address these issues, it is crucial to predict student performance at an early stage. This allows teachers to provide timely support and interventions to help students achieve their learning objectives.

[Impact of hypotension and global hypoperfusion in postoperative delirium: a pilot study in older adults undergoing open colon surgery].

Background: Post-operative delirium is a serious complication in patients undergoing major abdominal surgery. It remains unclear whether peri-operative hemodynamic and perfusion variables affect the risk for postoperative delirium. The objective of this pilot study was to evaluate the association between perfusion and hemodynamics peri-operative with the appearance of post-operative delirium.

[Early enteral nutrition in cancer patients subjected to a total gastrectomy].

Background: Total parenteral nutrition has a high cost and frequency of complications. Enteral feeding is a feasible alternative that can be started early in the postoperative period.

Aim: To assess digestive tolerance to early enteral feeding in cancer patients undergoing total gastrectomy and to compare early enteral feeding (EEF) with total parenteral nutrition plus enteral feeding (TPN + EF), initiated after overcoming postoperative ileus.