Estimation of Cancer Deaths Averted From Prevention, Screening, and Treatment Efforts, 1975-2020.

Importance: Cancer mortality has decreased over time, but the contributions of different interventions across the cancer control continuum to averting cancer deaths have not been systematically evaluated across major cancer sites.

Objective: To quantify the contributions of prevention, screening (to remove precursors [interception] or early detection), and treatment to cumulative number of cancer deaths averted from 1975 to 2020 for breast, cervical, colorectal, lung, and prostate cancers.

Design, Setting, And Participants: In this model-based study using population-level cancer mortality data, outputs from published models developed by the Cancer Intervention and Surveillance Modeling Network were extended to quantify cancer deaths averted through 2020.

The Supporting Emerging Adults With Diabetes (SEAD) Program: An Adult-Based Real-World Clinical Model That Improves Hospitalizations, Diabetes Technology Uptake, and Glycemic Outcomes in Underserved Young Adults With Type 1 Diabetes.

Objective: Underserved young adults (YA) with type 1 diabetes (T1D) experience the worst outcomes across the life span. We developed and integrated the Supporting Emerging Adults with Diabetes (SEAD) program into routine endocrinology care to address unmet social and medical challenges.

Research Design And Methods: This study was designed as a longitudinal cohort study, with prospective data collection over 4 years on YA in SEAD compared with usual endocrine care.

ChatGPT Performs Worse on USMLE-Style Ethics Questions Compared to Medical Knowledge Questions.

Objectives:  The main objective of this study is to evaluate the ability of the Large Language Model Chat Generative Pre-Trained Transformer (ChatGPT) to accurately answer the United States Medical Licensing Examination (USMLE) board-style medical ethics questions compared to medical knowledge-based questions. This study has the additional objectives of comparing the overall accuracy of GPT-3.5 to GPT-4 and assessing the variability of responses given by each version.

Breast Cancer Screening Using Mammography, Digital Breast Tomosynthesis, and Magnetic Resonance Imaging by Breast Density.

Importance: Information on long-term benefits and harms of screening with digital breast tomosynthesis (DBT) with or without supplemental breast magnetic resonance imaging (MRI) is needed for clinical and policy discussions, particularly for patients with dense breasts.

Objective: To project long-term population-based outcomes for breast cancer mammography screening strategies (DBT or digital mammography) with or without supplemental MRI by breast density.

Design, Setting, And Participants: Collaborative modeling using 3 Cancer Intervention and Surveillance Modeling Network (CISNET) breast cancer simulation models informed by US Breast Cancer Surveillance Consortium data.

The population-level effects of omitting chemotherapy guided by a 21-gene expression assay in node-positive breast cancer: a simulation modeling study.

Background: A recent trial showed that postmenopausal women diagnosed with hormone receptor-positive, human epidermal growth factor receptor-2 (HER2)-negative, lymph node-positive (1-3 nodes) breast cancer with a 21-gene recurrence score of ≤ 25 could safely omit chemotherapy. However, there are limited data on population-level long-term outcomes associated with omitting chemotherapy among diverse women seen in real-world practice.

Methods: We adapted an established, validated simulation model to generate the joint distributions of population-level characteristics of women diagnosed with early-stage breast cancer in the U.