Background: The precise effect and the quality of different cases used in dermatology problem-based learning (PBL) curricula are yet unclear.
Aim: To prospectively compare the impact of real patients, digital, paper PBL (PPBL) and traditional lecture-based learning (LBL) on academic results and student perceptions.
Methods: A total of 120 students were randomly allocated into either real-patients PBL (RPBL) group studied via real-patient cases, digital PBL (DPBL) group studied via digital-form cases, PPBL group studied via paper-form cases, or conventional group who received didactic lectures. Academic results were assessed through review of written examination, objective structured clinical examination and student performance scores. A five-point Likert scale questionnaire was used to evaluate student perceptions.
Results: Compared to those receiving lectures only, all PBL participants had better results for written examination, clinical examination and overall performance. Students in RPBL group exhibited better overall performance than those in the other two PBL groups. Real-patient cases were more effective in helping develop students' self-directed learning skills, improving their confidence in future patient encounters and encouraging them to learn more about the discussed condition, compared to digital and paper cases.
Conclusion: Both real patient and digital triggers are helpful in improving students' clinical problem-handling skills. However, real patients provide greater benefits to students.
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http://dx.doi.org/10.3109/0142159X.2012.719651 | DOI Listing |
Background: Primary care physicians (PCPs) and nurse practitioners play a key role in guiding caregivers on early peanut protein (PP) introduction, yet many lack adequate knowledge.
Aim Statement: This quality improvement study aimed to enhance understanding among PCPs and caregivers about evidence-based guidelines for early PP introduction in infants' diets.
Methods: Using the Stetler Model, PCP knowledge was evaluated through pre-test, educational video and some posttest material.
Sci Rep
January 2025
Institute for System Dynamics, University of Stuttgart, Waldburgstr. 19, 70563, Stuttgart, Germany.
Including sensor information in medical interventions aims to support surgeons to decide on subsequent action steps by characterizing tissue intraoperatively. With bladder cancer, an important issue is tumor recurrence because of failure to remove the entire tumor. Impedance measurements can help to classify bladder tissue and give the surgeons an indication on how much tissue to remove.
View Article and Find Full Text PDFAnn Clin Microbiol Antimicrob
January 2025
Department of Clinical Laboratory, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008, Hunan, China.
Background: The emergence of colistin resistance in carbapenem-resistant Klebsiella pneumoniae (CRKP) is a significant public health concern, as colistin has been the last resort for treating such infections. This study aimed to investigate the prevalence and molecular characteristics of colistin-resistant CRKP isolates in Central South China.
Methods: CRKP isolates from twelve hospitals in Central South China were screened for colistin resistance using broth microdilution.
Sci Rep
January 2025
Division of National Control of Communicable Diseases, Ministry of Health, Asmara, Eritrea.
Real-world data on treatment outcomes or the quality of large-scale chronic hepatitis B (CHB) treatment programs in sub-Saharan Africa (SSA) is extremely difficult to obtain. In this study, we aimed to provide data on the prevalence and incidence of mortality, loss to follow-up (LFTU), and their associated factors in patients with CHB in three treatment centres in Eritrea. Additional information includes baseline clinical profiles of CHB patients initiated on nucleos(t)ide analogue (NUCs) along with a comparison of treatment with Tenofovir disoproxil fumarate (TDF) vs.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Respiratory and Critical Care Medicine, Changhai Hospital, The Second Military Medical University, Shanghai, People's Republic of China.
In recent years, large amounts of researches showed that pulmonary embolism (PE) has become a common disease, and PE remains a clinical challenge because of its high mortality, high disability, high missed and high misdiagnosed rates. To address this, we employed an artificial intelligence-based machine learning algorithm (MLA) to construct a robust predictive model for PE. We retrospectively analyzed 1480 suspected PE patients hospitalized in West China Hospital of Sichuan University between May 2015 and April 2020.
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