IFEM model curriculum: emergency medicine learning outcomes for undergraduate medical education.

Background: The International Federation for Emergency Medicine (IFEM) published its model curriculum for medical student education in emergency medicine in 2009. Because of the evolving principles of emergency medicine and medical education, driven by societal, professional, and educational developments, there was a need for an update on IFEM recommendations. The main objective of the update process was creating Intended Learning Outcomes (ILOs) and providing tier-based recommendations.

: A web-based application for in-depth exploration of multi-omics data with brightfield histology.

Spatial -OMICS technologies facilitate the interrogation of molecular profiles in the context of the underlying histopathology and tissue microenvironment. Paired analysis of histopathology and molecular data can provide pathologists with otherwise unobtainable insights into biological mechanisms. To connect the disparate molecular and histopathologic features into a single workspace, we developed (unctional nit tate dentificati in WSIs [Whole Slide Images]), a web-based tool that provides users with a broad array of visualization and analytical tools including deep learning-based algorithms for in-depth interrogation of spatial -OMICS datasets and their associated high-resolution histology images.

Correction: Clinical reasoning education in the clerkship years: A cross-disciplinary national needs assessment.

[This corrects the article DOI: 10.1371/journal.pone.

Applying the master adaptive learner framework to just-in-time training of procedures.

Background: Just-in-time training (JITT) occurs in the clinical context when learners need immediate guidance for procedures due to a lack of proficiency or the need for knowledge refreshment. The master adaptive learner (MAL) framework presents a comprehensive model of transforming learners into adaptive experts, proficient not only in their current tasks but also in the ongoing development of lifelong skills. With the evolving landscape of procedural competence in emergency medicine (EM), trainees must develop the capacity to acquire and master new techniques consistently.

Assessing clinical competence: a multitrait-multimethod matrix construct validity study.

Education in Doctor of Medicine programs has moved towards an emphasis on clinical competency, with entrustable professional activities providing a framework of learning objectives and outcomes to be assessed within the clinical environment. While the identification and structured definition of objectives and outcomes have evolved, many methods employed to assess clerkship students' clinical skills remain relatively unchanged. There is a paucity of medical education research applying advanced statistical design and analytic techniques to investigate the validity of clinical skills assessment.

Taking More Society for Academic Emergency Medicine Practice Tests Does Not Lead to Improved National EM-M4 Exam Scores.

Introduction: Emergency medicine (EM) is a required clerkship for third-year medical students, and an elective EM acting internship (AI) is available to fourth-year students at our institution. The Society for Academic Emergency Medicine's (SAEM) National Emergency Medicine M4 Examination (EM-M4) is administered to students at the end of the EM AI experience. To prepare for the exam, students gain access to 23 practice tests available from SAEM.

Development of a lecture evaluation tool rooted in cognitive load theory: A modified Delphi study.

Background: Didactics play a key role in medical education. There is no standardized didactic evaluation tool to assess quality and provide feedback to instructors. Cognitive load theory provides a framework for lecture evaluations.

Clinical reasoning education in the clerkship years: A cross-disciplinary national needs assessment.

Background: Improving clinical reasoning education has been identified as an important strategy to reduce diagnostic error-an important cause of adverse patient outcomes. Clinical reasoning is fundamental to each specialty, yet the extent to which explicit instruction in clinical reasoning occurs across specialties in the clerkship years remains unclear.

Method: The Alliance for Clinical Education (ACE) Clinical Reasoning Workgroup and the Directors of Clinical Skills Courses (DOCS) Clinical Reasoning Workgroup collaborated to develop a clinical reasoning needs assessment survey.

A tool for federated training of segmentation models on whole slide images.

The largest bottleneck to the development of convolutional neural network (CNN) models in the computational pathology domain is the collection and curation of diverse training datasets. Training CNNs requires large cohorts of image data, and model generalizability is dependent on training data heterogeneity. Including data from multiple centers enhances the generalizability of CNN-based models, but this is hindered by the logistical challenges of sharing medical data.

PodoCount: A Robust, Fully Automated, Whole-Slide Podocyte Quantification Tool.

Introduction: Podocyte depletion is a histomorphologic indicator of glomerular injury and predicts clinical outcomes. Podocyte estimation methods or podometrics are semiquantitative, technically involved, and laborious. Implementation of high-throughput podometrics in experimental and clinical workflows necessitates an automated podometrics pipeline.

NuCLS: A scalable crowdsourcing approach and dataset for nucleus classification and segmentation in breast cancer.

Background: Deep learning enables accurate high-resolution mapping of cells and tissue structures that can serve as the foundation of interpretable machine-learning models for computational pathology. However, generating adequate labels for these structures is a critical barrier, given the time and effort required from pathologists.

Results: This article describes a novel collaborative framework for engaging crowds of medical students and pathologists to produce quality labels for cell nuclei.

Validity evidence for an instrument for cognitive load for virtual didactic sessions.

Background: COVID necessitated the shift to virtual resident instruction. The challenge of learning via virtual modalities has the potential to increase cognitive load. It is important for educators to reduce cognitive load to optimize learning, yet there are few available tools to measure cognitive load.

A cloud-based tool for federated segmentation of whole slide images.

It is commonly known that diverse datasets of WSIs are beneficial when training convolutional neural networks, however sharing medical data between institutions is often hindered by regulatory concerns. We have developed a cloud-based tool for federated WSI segmentation, allowing collaboration between institutions without the need to directly share data. To show the feasibility of federated learning on pathology data in the real world, We demonstrate this tool by segmenting IFTA from three institutions and show that keeping the three datasets separate does not hinder segmentation performance.

Explainable nucleus classification using Decision Tree Approximation of Learned Embeddings.

Motivation: Nucleus detection, segmentation and classification are fundamental to high-resolution mapping of the tumor microenvironment using whole-slide histopathology images. The growing interest in leveraging the power of deep learning to achieve state-of-the-art performance often comes at the cost of explainability, yet there is general consensus that explainability is critical for trustworthiness and widespread clinical adoption. Unfortunately, current explainability paradigms that rely on pixel saliency heatmaps or superpixel importance scores are not well-suited for nucleus classification.

PodoSighter: A Cloud-Based Tool for Label-Free Podocyte Detection in Kidney Whole-Slide Images.

Background: Podocyte depletion precedes progressive glomerular damage in several kidney diseases. However, the current standard of visual detection and quantification of podocyte nuclei from brightfield microscopy images is laborious and imprecise.

Methods: We have developed PodoSighter, an online cloud-based tool, to automatically identify and quantify podocyte nuclei from giga-pixel brightfield whole-slide images (WSIs) using deep learning.

Effect of Perceived Level of Interaction on Faculty Evaluations of 3rd Year Medical Students.

Introduction: Several factors are known to affect the way clinical performance evaluations (CPEs) of medical students are completed by supervising physicians. We sought to explore the effect of faculty perceived "level of interaction" (LOI) on these evaluations.

Methods: Our third-year CPE requires evaluators to identify perceived LOI with each student as low, moderate, or high.

Under the Radar: A Case Report of a Missed Aortoenteric Fistula.

Introduction: An aortoenteric fistula (AEF) is an abnormal connection between the aorta and the gastrointestinal tract that develops due to a pathologic cause. It is a rare, but life-threatening, cause of gastrointestinal (GI) bleeding. Although no single imaging modality exists that definitively diagnoses AEF, computed tomography angiography (CTA) of the abdomen and pelvis is the preferred initial test due to widespread availability and efficiency.

User friendly, cloud based, whole slide image segmentation.

Convolutional neural networks, the state of the art for image segmentation, have been successfully applied to histology images by many computational researchers. However, the translatability of this technology to clinicians and biological researchers is limited due to the complex and undeveloped user interface of the code, as well as the extensive computer setup required. We have developed a plugin for segmentation of whole slide images (WSIs) with an easy to use graphical user interface.

A Distributed System Improves Inter-Observer and AI Concordance in Annotating Interstitial Fibrosis and Tubular Atrophy.

Histologic examination of interstitial fibrosis and tubular atrophy (IFTA) is critical to determine the extent of irreversible kidney injury in renal disease. The current clinical standard involves pathologist's visual assessment of IFTA, which is prone to inter-observer variability. To address this diagnostic variability, we designed two case studies (CSs), including seven pathologists, using HistomicsTK- a distributed system developed by Kitware Inc.

The Impact of Shortened Clinical Clerkships on Medical Student Performance and Clerkship Assessment.

Intro: Medical schools sometimes need to adjust the length of third-year clinical clerkships. The literature surrounding the effects of shortened clerkships on student experience and performance is mixed.

Methods: Our medical school shortened the third year by an average of 20% per clerkship to accommodate a curricular re-design in 2018-2019.

The GI Simulated Clinic: A Clinical Reasoning Exercise Supporting Medical Students' Basic and Clinical Science Integration.

Introduction: Cognitive integration is required to perform clinical decision-making tasks, even in the preclinical curriculum of medical school. Simulation supports students' cognitive integration by providing practical application of basic science knowledge in a relevant clinical context. To address the need for integrative activities in our curriculum, we implemented a simulated clinic exercise with cases representing gastrointestinal diseases for first-year medical students.