Why do people still make anti-D over 50 years after the introduction of Rho(D) immune globulin? A Biomedical Excellence for Safer Transfusion (BEST) Collaborative study.

Background: Rho(D) immune globulin (RhIg) is used to reduce RhD alloimmunization in pregnancy. This study describes potential causes for RhD alloimmunization after the development and implementation of RhIg.

Study Design And Methods: This retrospective descriptive study investigated RhD-negative patients born in 1965-2005 with anti-D newly identified during 2018-2022.

Emergency department visits and hospitalizations after a diagnosis of angina with no obstructive coronary artery disease (ANOCA).

Introduction: Angina with no obstructive coronary artery disease (ANOCA) presents diagnostic and treatment challenges, significantly burdening healthcare resources. This study assessed emergency department (ED) visits and hospitalizations and factors associated with these outcomes following ANOCA and stable angina (SA) with obstructive coronary artery disease (CAD) diagnoses.

Methods: A retrospective cohort of individuals who had their first invasive cardiac catheterization for chest pain in Alberta from 2002 to 2017 was extracted retrospectively from the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease (APPROACH) database.

Prognostic models for survival predictions in advanced cancer patients: a systematic review and meta-analysis.

Background: Prognostication of survival among patients with advanced cancer is essential for palliative care (PC) planning. The implementation of a clinical point-of-care prognostic model may inform clinicians and facilitate decision-making. While early PC referral yields better clinical outcomes, actual referral time differs by clinical contexts and accessible.

Developing a named entity framework for thyroid cancer staging and risk level classification using large language models.

We developed a named entity (NE) framework for information extraction from semi-structured clinical notes retrieved from The Cancer Genome Atlas-Thyroid Cancer (TCGA-THCA) database and examined Large Language Models (LLMs) strategies to classify the 8 edition of American Joint Committee on Cancer (AJCC) staging and American Thyroid Association (ATA) risk category for patients with well-differentiated thyroid cancer. The NE framework consisted of annotation guidelines development, ground truth labelling, prompting approaches, and evaluation codes. Four LLMs (Mistral-7B-Instruct, Llama-3.

Deep learning image enhancement for confident diagnosis of TMJ osteoarthritis in zero-TE MR imaging.

Objective: This study aimed to evaluate the effectiveness of deep learning method for denoising and artifact reduction (AR) in zero-TE (ZTE) magnetic resonance imaging (MRI). Also, Clinical applicability was evaluated by comparing image diagnosis to the temporomandibular joint (TMJ) cone-beam computed tomography (CBCT).

Methods: For thirty patients CBCT and routine ZTE-MRI data was collected, and an additional reduced scan time-ZTE-MRI was also obtained.