Reference Ranges for All: Implementing Reference Ranges for Transgender and Nonbinary Patients.

Objectives:  This study aimed to highlight the necessity of developing and implementing appropriate reference ranges for transgender and nonbinary (TGNB) patient populations to minimize misinterpretation of laboratory results and ensure equitable health care.

Case Report:  We describe a situation where a TGNB patient's abnormal laboratory values were not flagged due to undefined reference ranges for gender "X" in the Laboratory Information System (LIS). Implementation of additional reference ranges mapped to sex label "X" showed significant improvement in flagging abnormal lab results, utilizing sex-invariant reporting as an interim solution while monitoring developments on TGNB-specific reference ranges.

Evaluating Large Language Models in extracting cognitive exam dates and scores.

Ensuring reliability of Large Language Models (LLMs) in clinical tasks is crucial. Our study assesses two state-of-the-art LLMs (ChatGPT and LlaMA-2) for extracting clinical information, focusing on cognitive tests like MMSE and CDR. Our data consisted of 135,307 clinical notes (Jan 12th, 2010 to May 24th, 2023) mentioning MMSE, CDR, or MoCA.

Validation and clinical discovery demonstration of breast cancer data from a real-world data extraction platform.

Objective: To validate and demonstrate the clinical discovery utility of a novel patient-mediated, medical record collection and data extraction platform developed to improve access and utilization of real-world clinical data.

Materials And Methods: Clinical variables were extracted from the medical records of 1011 consented patients with breast cancer. To validate the extracted data, case report forms completed using the structured data output of the platform were compared to manual chart review for 50 randomly-selected patients with metastatic breast cancer.

NetMe 2.0: a web-based platform for extracting and modeling knowledge from biomedical literature as a labeled graph.

Motivation: The rapid increase of bio-medical literature makes it harder and harder for scientists to keep pace with the discoveries on which they build their studies. Therefore, computational tools have become more widespread, among which network analysis plays a crucial role in several life-science contexts. Nevertheless, building correct and complete networks about some user-defined biomedical topics on top of the available literature is still challenging.

Evaluating Large Language Models in Extracting Cognitive Exam Dates and Scores.

Importance: Large language models (LLMs) are crucial for medical tasks. Ensuring their reliability is vital to avoid false results. Our study assesses two state-of-the-art LLMs (ChatGPT and LlaMA-2) for extracting clinical information, focusing on cognitive tests like MMSE and CDR.