Background: There is an emerging perspective that it is not sufficient to just assess skin exposure to physical and chemical stressors in workplaces, but that it is also important to assess the condition, i.e. skin barrier function of the exposed skin at the time of exposure. The workplace environment, representing a non-clinical environment, can be highly variable and difficult to control, thereby presenting unique measurement challenges not typically encountered in clinical settings.
Methods: An expert working group convened a workshop as part of the 5th International Conference on Occupational and Environmental Exposure of Skin to Chemicals (OEESC) to develop basic guidelines and best practices (based on existing clinical guidelines, published data, and own experiences) for the in vivo measurement of transepidermal water loss (TEWL) and skin hydration in non-clinical settings with specific reference to the workplace as a worst-case scenario.
Results: Key elements of these guidelines are: (i) to minimize or recognize, to the extent feasible, the influences of relevant endogenous-, exogenous-, environmental- and measurement/instrumentation-related factors; (ii) to measure TEWL with a closed-chamber type instrument; (iii) report results as a difference or percent change (rather than absolute values); and (iv) accurately report any notable deviations from this guidelines.
Conclusion: It is anticipated that these guidelines will promote consistent data reporting, which will facilitate inter-comparison of study results.
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http://dx.doi.org/10.1111/srt.12037 | DOI Listing |
NPJ Digit Med
January 2025
Department of Plastic and Reconstructive Surgery, Erasmus MC University Medical Center, Rotterdam, the Netherlands.
This systematic review explores machine learning (ML) applications in surgical motion analysis using non-optical motion tracking systems (NOMTS), alone or with optical methods. It investigates objectives, experimental designs, model effectiveness, and future research directions. From 3632 records, 84 studies were included, with Artificial Neural Networks (38%) and Support Vector Machines (11%) being the most common ML models.
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December 2024
Clinical Laboratory Sciences, Jouf University, Jouf, SAU.
Background: With increasing reliance on digital devices, concerns about their impact on mental health have grown, particularly among young adults.
Aim: This study aims to evaluate the impact of a digital detox intervention on reducing anxiety and depression among young adults across diverse demographic backgrounds.
Methods: A pre-test, followed by a digital detox intervention, and a post-test using an online survey was carried out.
BJPsych Open
January 2025
Department of Psychiatry, Faculty of Medicine, University of Southampton, Southampton, UK.
Background: Alcohol use in autism spectrum disorder (ASD) is under-researched. Previous reviews have explored substance use as a whole, but this neglects individual characteristics unique to different substances. Alcohol use in non-clinical samples is associated with diverse responses.
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December 2024
Conversaurus, Richmond, UK.
Communication is fundamental to effective healthcare. Misunderstandings can increase distress, risks and costs. Clean Language is a precision questioning technique-with specific Clean Language questions which minimise assumptions and bias.
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January 2025
School of Translational Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia.
Background: Discrete choice experiments (DCEs) are increasingly used to inform the design of health products and services. It is essential to understand the extent to which DCEs provide reliable predictions outside of experimental settings in real-world decision-making situations. We aimed to compare the prediction accuracy of stated preferences with real-world choices, as modelled from DCE data.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!