Exploring the Interplay of Dataset Size and Imbalance on CNN Performance in Healthcare: Using X-rays to Identify COVID-19 Patients.

Introduction: Convolutional Neural Network (CNN) systems in healthcare are influenced by unbalanced datasets and varying sizes. This article delves into the impact of dataset size, class imbalance, and their interplay on CNN systems, focusing on the size of the training set versus imbalance-a unique perspective compared to the prevailing literature. Furthermore, it addresses scenarios with more than two classification groups, often overlooked but prevalent in practical settings.

Worse Morning Energy Profiles Are Associated with Significant Levels of Stress and Decrements in Resilience in Patients Receiving Chemotherapy.

Objectives: Evidence suggests that lower levels of morning energy are associated with higher levels of stress and lower levels of resilience in patients receiving chemotherapy. Study purposes were to identify subgroups of patients with distinct morning energy profiles; evaluate for differences among the profiles in demographic and clinical characteristics, as well as measures of stress, resilience, and coping.

Methods: A total of 1,343 outpatients receiving chemotherapy completed a demographic questionnaire and measures of global, cancer-related, and cumulative life stress, and resilience at study enrollment.

Effect of climate on emergency department admissions for renal colic: a comparative analysis of sedentary and non-sedentary lifestyles.

Objective: To investigate the impact of climate and seasonal variations on emergency department (ED) admissions for renal colic, while specifically comparing the differences between individuals with sedentary and non-sedentary lifestyles.

Patients And Methods: A retrospective, single center study was conducted. Between the years 2017- 2020, medical records of patients admitted to the ED with renal colic, found to harbor ureteric stones on CT scans, were examined.

Cost, Usability, Credibility, Fairness, Accountability, Transparency, and Explainability Framework for Safe and Effective Large Language Models in Medical Education: Narrative Review and Qualitative Study.

Background: The world has witnessed increased adoption of large language models (LLMs) in the last year. Although the products developed using LLMs have the potential to solve accessibility and efficiency problems in health care, there is a lack of available guidelines for developing LLMs for health care, especially for medical education.

Objective: The aim of this study was to identify and prioritize the enablers for developing successful LLMs for medical education.