Journal of evaluation in clinical practice
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Artificial Intelligence (AI) large language models (LLM) are tools capable of generating human-like text responses to user queries across topics. The use of these language models in various medical contexts is currently being studied. However, the performance and content quality of these language models have not been evaluated in specific medical fields. ⋯ Although LLMs provide parents with high-accuracy information about CKD, their use is limited compared with that of a reference source. The limitations in the performance of LLMs can lead to misinformation and potential misinterpretations. Therefore, patients and parents should exercise caution when using these models.
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Review Meta Analysis
Clinical decision support systems in addiction and concurrent disorders: A systematic review and meta-analysis.
This review aims to synthesise the literature on the efficacy, evolution, and challenges of implementing Clincian Decision Support Systems (CDSS) in the realm of mental health, addiction, and concurrent disorders. ⋯ CDSS shows promise in mental health and addiction treatment but requires a nuanced approach for effective and ethical implementation. The results emphasise the need for continued research to ensure optimised and equitable use in healthcare settings.
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This is a protocol of a scoping review that will aim to synthesise methodological evidence on formulating plain language versions of recommendations from guidelines both for clinical practice and for public health. ⋯ We will conduct a scoping review based on this protocol.
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Low back pain (LBP) is a common condition with a significant societal burden. Manual therapy is an effective treatment for LBP and recommended in clinical practice guidelines. While the quantity of literature supporting the use of manual therapy is large, the methodological quality and transparency of this collective work are unclear. ⋯ Approximately half of the trials were not registered. Of those registered, only half were registered prospectively. Substantial discrepancies existed between registered and published outcomes that were never addressed by the authors, raising questions about potential bias. Transparency can be improved through more stringent requirements during manuscript submission to journals, and better reporting of the rationale for discrepancies between registration and publication.
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Visitation has a positive effect on patients and families, yet, it can disrupt intensive care unit (ICU) care and increase the risk of patient infections, which previously favoured face-to-face visits. The coronavirus disease 2019 (COVID-19) pandemic has raised the importance of virtual visits and led to their widespread adoption globally, there are still many implementation barriers that need to be improved. Therefore, this review aimed to explore the use of ICU virtual visit technology during the COVID-19 pandemic and the barriers and facilitators of virtual visits to improve virtual visits in ICUs. ⋯ This review identified key facilitating factors and barriers to ICU virtual visits, which can foster the development of infrastructure, virtual visiting workflows, guidelines, policies and visiting systems to improve ICU virtual visiting services. Further studies are necessary to identify potential solutions to the identified barriers.