Purpose: The aim of the study was to introduce our experience of establish task-based learning outcomes for core clinical clerkships.
Methods: We first define our educational goal and objectives of the clinical clerkship curriculum according to knowledge, cognitive function and skill, and attitude. We selected clinical presentations and related diseases with expert panels and allocated them to core clinical departments. We classified doctor's tasks into 6 categories: history taking, physical examination, diagnostic plan, therapeutic plan, acute and emergent management, and prevention and patient education. We described learning outcomes by task using behavioral terms.
Results: We established goals and objectives for students to achieve clinical competency on a primary care level. We selected 75 clinical presentations and described 377 learning outcomes.
Conclusion: Our process can benefit medical schools that offer outcome-based medical education, especially for clinical clerkships. To drive effective clerkships, a supportive system including assessment and faculty development should be implemented.
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http://dx.doi.org/10.3946/kjme.2012.24.1.31 | DOI Listing |
Breast Cancer
January 2025
Division of Breast and Endocrine Surgery, Department of Surgery, School of Medicine, Hyogo Medical University, 1-1 Mukogawa-cho, Nishinomiya, Hyogo, 663-8501, Japan.
Purpose: The aim of this study was to examine the clinical utility of tumor-infiltrating lymphocytes (TILs) evaluated by "average" and "hot-spot" methods in breast cancer patients.
Methods: We examined 367 breast cancer patients without neoadjuvant chemotherapy (NAC) by average and hot-spot methods to determine the consistency of TIL scores between biopsy and surgical specimens. TIL scores before NAC were also compared with the pathological complete response (pCR) rate and clinical outcomes in 144 breast cancer patients that received NAC.
J Med Syst
January 2025
Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea.
Optimizing operating room (OR) utilization is critical for enhancing hospital management and operational efficiency. Accurate surgical case duration predictions are essential for achieving this optimization. Our study aimed to refine the accuracy of these predictions beyond traditional estimation methods by developing Random Forest models tailored to specific surgical departments.
View Article and Find Full Text PDFNeurosurg Rev
January 2025
Kobayashi Hospital, 510 Imaichi, Izumo City, Shimane, 693-0001, Japan.
Adverse effects of advanced age and poor initial neurological status on outcomes of patients with aneurysmal subarachnoid hemorrhage (SAH) have been documented. While a predictive model of the non-linear correlation between advanced age and clinical outcome has been reported, no previous model has been validated. Therefore, we created a prediction model of the non-linear correlation between advanced age and clinical outcome by machine learning and validated it using a separate cohort.
View Article and Find Full Text PDFRadiology
January 2025
From the Department of Cardiology (T.P., K.H., T.G., A.L., E.G., A.U., J.G.D., P.H.), MIRACL.ai (Multimodality Imaging for Research and Analysis Core Laboratory: and Artificial Intelligence) (T.P., S.T., K.H., T.G., A.L., E.G., A.U., J.G.D., P.H.), Inserm MASCOT-UMRS 942 (T.P., K.H., T.A.S., T.G., A.L., E.G., A.U., J.G.D., P.H.), and Department of Radiology (T.P., V.B., L.H., T.G.), Université Paris Cité, University Hospital of Lariboisière, Assistance Publique-Hôpitaux de Paris, Paris, France; Cardiovascular Magnetic Resonance Laboratory (T.P., T.H., T.U., F.S., S.C., P.G., J.G.) and Cardiac Computed Tomography Laboratory (T.P., T.H., T.L., B.C., T.U., F.S., S.C., H.B., A.N., M.A., P.G., J.G.), Hôpital Privé Jacques Cartier, Institut Cardiovasculaire Paris Sud, Ramsay Santé, 6 Avenue du Noyer Lambert, 91300 Massy, France; Scientific Partnerships, Siemens Healthcare France, Saint-Denis, France (S.T.); Department of Cardiology, Hôpital Universitaire de Bruxelles-Hôpital Erasme, Brussels, Belgium (A.U.); and Department of Cardiovascular Imaging, American Hospital of Paris, Neuilly, France (O.V., M.S.).
Background Multimodality imaging is essential for personalized prognostic stratification in suspected coronary artery disease (CAD). Machine learning (ML) methods can help address this complexity by incorporating a broader spectrum of variables. Purpose To investigate the performance of an ML model that uses both stress cardiac MRI and coronary CT angiography (CCTA) data to predict major adverse cardiovascular events (MACE) in patients with newly diagnosed CAD.
View Article and Find Full Text PDFJ Am Coll Surg
January 2025
Department of Surgery, Stanford University, Stanford, CA.
Background: Motion-tracking has been shown to correlate with expert and novice performance but has not been used for skill development. For skill development, performance goals must be defined. We hypothesize that using wearable sensor technology, motion tracking outcomes can be identified in those deemed practice-ready and used as benchmarks for precision learning.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!