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International Expert-Based Consensus Definition, Classification Criteria, and Minimum Data Elements for Osteoradionecrosis of the Jaw: An Inter-Disciplinary Modified Delphi Study.
Int J Radiat Oncol Biol Phys
January 2025
Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, USA.
Purpose: Osteoradionecrosis of the jaw (ORNJ) is a severe iatrogenic disease characterized by bone death after radiation therapy (RT) to the head and neck. With over 9 published definitions and at least 16 classification systems, the true incidence and severity of ORNJ are obscured by lack of a standard for disease definition and severity assessment, leading to inaccurate estimation of incidence, reporting ambiguity, and likely under-diagnosis worldwide. This study aimed to achieve consensus on an explicit definition and phenotype of ORNJ and related precursor states through data standardization to facilitate effective diagnosis, monitoring, and multidisciplinary management of ORNJ.
View Article and Find Full Text PDFLarge-scale and long-term wildlife research and monitoring using camera traps: a continental synthesis.
Biol Rev Camb Philos Soc
January 2025
Wildlife Observatory of Australia (WildObs), Queensland Cyber Infrastructure Foundation (QCIF), Brisbane, Queensland, 4072, Australia.
Camera traps are widely used in wildlife research and monitoring, so it is imperative to understand their strengths, limitations, and potential for increasing impact. We investigated a decade of use of wildlife cameras (2012-2022) with a case study on Australian terrestrial vertebrates using a multifaceted approach. We (i) synthesised information from a literature review; (ii) conducted an online questionnaire of 132 professionals; (iii) hosted an in-person workshop of 28 leading experts representing academia, non-governmental organisations (NGOs), and government; and (iv) mapped camera trap usage based on all sources.
View Article and Find Full Text PDFA Comprehensive Review of Injury Causation Analysis Methodology for the Assessment of Workers' Compensation and Motor Vehicle Collision Injuries.
J Am Acad Orthop Surg
November 2024
From the Biodynamic Research Corporation, San Antonio, TX (Santos, Watson), OrthoLegal, Marina Del Rey, CA (Dixon), and Department of Orthopaedics and Rehabilitation (Whang), Yale University School of Medicine, New Haven, CT.
Orthopaedic surgeons are frequently involved in treating patients with conditions resulting from occupational injuries or trauma from motor vehicle collisions. These circumstances may lead to disputes that are subject to litigation or medicolegal determinations by state agencies. As musculoskeletal experts, orthopaedic surgeons are frequently asked to opine on the causation and extent of injury in these patients.
View Article and Find Full Text PDFSix-Month Outcomes in the Long-Term Outcomes After the Multisystem Inflammatory Syndrome in Children Study.
JAMA Pediatr
January 2025
Department of Cardiology, Harvard Medical School and Boston Children's Hospital, Boston, Massachusetts.
Importance: Multisystem inflammatory syndrome in children (MIS-C) is a life-threatening complication of COVID-19 infection. Data on midterm outcomes are limited.
Objective: To characterize the frequency and time course of cardiac dysfunction (left ventricular ejection fraction [LVEF] <55%), coronary artery aneurysms (z score ≥2.
The Impact of Artificial Intelligence on Healthcare: A Comprehensive Review of Advancements in Diagnostics, Treatment, and Operational Efficiency.
Health Sci Rep
January 2025
Department of Microbiology Dr D. Y. Patil Medical College, Hospital and Research Centre, Dr D. Y. Patil Vidyapeeth (Deemed-to-be-University) Pune Maharashtra India.
Background And Aims: Artificial Intelligence (AI) beginning to integrate in healthcare, is ushering in a transformative era, impacting diagnostics, altering personalized treatment, and significantly improving operational efficiency. The study aims to describe AI in healthcare, including important technologies like robotics, machine learning (ML), deep learning (DL), and natural language processing (NLP), and to investigate how these technologies are used in patient interaction, predictive analytics, and remote monitoring. The goal of this review is to present a thorough analysis of AI's effects on healthcare while providing stakeholders with a road map for navigating this changing environment.
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