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The study aimed to develop a computational fluid dynamics-based mobile wound irrigation education program and explore changes in irrigation pressure control, wound irrigation-related knowledge and performance confidence in syringe-based wound irrigation. This study used a single-group pre–post design. A computational fluid dynamics-based mobile wound irrigation program was developed following the Analysis, Design, Development, Implementation, and Evaluation model. The program enabled learners to manipulate irrigation variables and visualize pressure distribution in real time. Thirty-four participants were recruited. Irrigation pressure was measured using a load cell-based device, and knowledge and performance confidence were assessed pre- and post-intervention. Data were analysed using paired t-tests and content analysis. The mean irrigation pressure increased significantly, although the post-intervention mean remained below the recommended pressure range and the proportion of participants achieving the recommended range rose from 0% to 44%. Knowledge and performance confidence also improved significantly. Qualitative findings indicated enhanced understanding of performance standards, improved technical awareness and reduced uncertainty during skill execution. Participation in the computational fluid dynamics-based mobile education program was associated with improvements in irrigation pressure control, related knowledge and performance confidence in syringe-based wound irrigation. These findings should be interpreted as preliminary because of the single-group pre–post design. Numerical visualization and real-time feedback may be useful educational strategies for facilitating the transition from experience-based skill performance to data-driven practice.
Trial Registration: Clinical Research Information Service (CRIS), Republic of Korea: KCT0011256.
This study aimed to develop a prediction model for the occurrence of medical adhesive-related skin injuries (MARSIs) based on electronic medical records (EMRs) of adult patients who underwent degenerative spine surgery. This study used the EMR data of adult patients who underwent degenerative spine surgery at a university hospital in Seoul between January 2020 and December 2024. Seven machine learning algorithms and the SuperLearner algorithm were used to evaluate the performance of the SuperLearner model. Performance was focused on the area under the curve (AUC), accuracy, sensitivity, specificity, precision and F1 score. Among the machine learning algorithms, the RuleFit algorithm showed the best performance, with an AUC of 0.723, accuracy of 0.689, sensitivity of 0.959, specificity of 0.276, precision of 0.762 and F1 score of 0.789. In contrast, predicting MARSI using the SuperLearner algorithm had an AUC of 0.951, accuracy of 0.834, sensitivity of 0.635, specificity of 0.964, precision of 0.921 and F1 score of 0.752. This study provides practical evidence for the early identification of high-risk patients and establishment of customized nursing plans by presenting a MARSI prediction model using the SuperLearner ensemble. Future research is recommended to verify the external validity of the model through prospective studies and integration of clinical decision support systems.
Trial Registration: ClinicalTrials.gov Identifier KCT0010601.
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