Background: Metal fabrication workers experience high rates of traumatic occupational injuries. Machine operators in particular face high risks, often stemming from the absence or improper use of machine safeguarding or the failure to implement lockout procedures.
Methods: The National Machine Guarding Program (NMGP) was a translational research initiative implemented in conjunction with two workers' compensation insures. Insurance safety consultants trained in machine guarding used standardized checklists to conduct a baseline inspection of machine-related hazards in 221 business.
Results: Safeguards at the point of operation were missing or inadequate on 33% of machines. Safeguards for other mechanical hazards were missing on 28% of machines. Older machines were both widely used and less likely than newer machines to be properly guarded. Lockout/tagout procedures were posted at only 9% of machine workstations.
Conclusions: The NMGP demonstrates a need for improvement in many aspects of machine safety and lockout in small metal fabrication businesses.
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http://dx.doi.org/10.1002/ajim.22514 | DOI Listing |
Lancet Reg Health West Pac
January 2025
State Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Science, Beijing, PR China.
Background: As natural reservoirs of diverse pathogens, small mammals are considered a key interface for guarding public health due to their wide geographic distribution, high density and frequent interaction with humans.
Methods: All formally recorded natural occurrences of small mammals (Order: Rodentia, Eulipotyphla, Lagomorpha, and Scandentia) and their associated microbial infections in China were searched in the English and Chinese literature spanning from 1950 to 2021 and geolocated. Machine learning models were applied to determine ecological drivers for the distributions of 45 major small mammal species and two common rodent-borne diseases (RBDs), and model-predicted potential risk locations were mapped.
J Funct Biomater
November 2024
King Abdullah International Medical Research Center, Riyadh 11481, Saudi Arabia.
The aim of the study is to assess the impact of mechanical surface treatments on the shear bond strength (SBS) of orthodontic brackets bonded to three-dimensional (3D) printed and milled CAD/CAM provisional materials. Sixty cylindrical samples were fabricated for each provisional material. Samples were treated with one of the following surface treatments: aluminum oxide airborne particle abrasion, diamond bur rotary instrument roughening, and phosphoric acid etching (control).
View Article and Find Full Text PDFCureus
November 2024
Department of Trauma and Orthopaedics, Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, GBR.
Introduction Orthopaedic surgery frequently involves the use of intra-operative radiographs, commonly taken with surgeons standing in close proximity to the X-ray machine. Radiation training and appropriate radiation protection minimise the harm that surgeons can face from ionising radiation. This study evaluates the current state of radiation training and protective equipment available to orthopaedic surgeons in the East of England.
View Article and Find Full Text PDFInteract J Med Res
December 2024
Department of Respiratory Therapy, College of Applied Medical Sciences, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia.
Background: Artificial intelligence is experiencing rapid growth, with continual innovation and advancements in the health care field.
Objective: This study aims to evaluate the application of artificial intelligence technologies across various domains of respiratory care.
Methods: We conducted a narrative review to examine the latest advancements in the use of artificial intelligence in the field of respiratory care.
Curr Heart Fail Rep
December 2024
Department of Cardiology, Division Heart & Lungs, University Medical Centre Utrecht, University Utrecht, Utrecht, the Netherlands.
Purpose Of Review: This review aims to explore the emerging potential of artificial intelligence (AI) in refining risk prediction, clinical diagnosis, and treatment stratification for cardiomyopathies, with a specific emphasis on arrhythmogenic cardiomyopathy (ACM).
Recent Findings: Recent developments highlight the capacity of AI to construct sophisticated models that accurately distinguish affected from non-affected cardiomyopathy patients. These AI-driven approaches not only offer precision in risk prediction and diagnostics but also enable early identification of individuals at high risk of developing cardiomyopathy, even before symptoms occur.
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