Coronavirus Disease 2019 (COVID-19) Real World Data Infrastructure: A Big-Data Resource for Study of the Impact of COVID-19 in Patient Populations With Immunocompromising Conditions.

Background: We developed a United States-based real-world data resource to better understand the continued impact of the coronavirus disease 2019 (COVID-19) pandemic on immunocompromised patients, who are typically underrepresented in prospective studies and clinical trials.

Methods: The COVID-19 Real World Data infrastructure (CRWDi) was created by linking and harmonizing de-identified HealthVerity medical and pharmacy claims data from 1 December 2018 to 31 December 2023, with severe acute respiratory syndrome coronavirus 2 virologic and serologic laboratory data from major commercial laboratories and Northwell Health; COVID-19 vaccination data; and, for patients with cancer, 2010 to 2021 National Cancer Institute Surveillance, Epidemiology, and End Results registry data.

Results: The CRWDi contains 4 cohorts: patients with cancer; patients with rheumatic diseases receiving pharmacotherapy; noncancer solid organ and hematopoietic stem cell transplant recipients; and people from the general population including adults and pediatric patients.

Heterogeneity of the Treatment Effect with PARP Inhibitors in Metastatic Castration-resistant Prostate Cancer: A Living Interactive Systematic Review and Meta-analysis.

Background And Objective: Selection of patients harboring mutations in homologous recombination repair (HRR) genes for treatment with a PARP inhibitor (PARPi) is challenging in metastatic castration-resistant prostate cancer (mCRPC). To gain further insight, we quantitatively assessed the differential efficacy of PARPi therapy among patients with mCRPC and different HRR gene mutations.

Methods: This living meta-analysis (LMA) was conducted using the Living Interactive Evidence synthesis framework.

Collaborative large language models for automated data extraction in living systematic reviews.

Objective: Data extraction from the published literature is the most laborious step in conducting living systematic reviews (LSRs). We aim to build a generalizable, automated data extraction workflow leveraging large language models (LLMs) that mimics the real-world 2-reviewer process.

Materials And Methods: A dataset of 10 trials (22 publications) from a published LSR was used, focusing on 23 variables related to trial, population, and outcomes data.

Large Language Models and Large Multimodal Models in Medical Imaging: A Primer for Physicians.

Large language models (LLMs) are poised to have a disruptive impact on health care. Numerous studies have demonstrated promising applications of LLMs in medical imaging, and this number will grow as LLMs further evolve into large multimodal models (LMMs) capable of processing both text and images. Given the substantial roles that LLMs and LMMs will have in health care, it is important for physicians to understand the underlying principles of these technologies so they can use them more effectively and responsibly and help guide their development.

Defining the Cost of Arthroscopic Rotator Cuff Repair: A Multicenter, Time-Driven Activity-Based Costing and Cost Optimization Investigation.

Background: Rotator cuff repair (RCR) is a frequently performed outpatient orthopaedic surgery, with substantial financial implications for health-care systems. Time-driven activity-based costing (TDABC) is a method for nuanced cost analysis and is a valuable tool for strategic health-care decision-making. The aim of this study was to apply the TDABC methodology to RCR procedures to identify specific avenues to optimize cost-efficiency within the health-care system in 2 critical areas: (1) the reduction of variability in the episode duration, and (2) the standardization of suture anchor acquisition costs.