Background: Cognitive impairment after anaesthesia and surgery is a recognised consequence. This often leads to poor health outcomes and increases healthcare resource utilisation and associated costs, especially in elderly people. However, thus far, there have not been any effective therapies for managing postoperative cognitive dysfunction (POCD). Furthermore, research on the association of multimodal warming with POCD and the clinical outcomes in older patients after gynaecological surgery has not been rigorous. For these reasons, our investigation aims to evaluate whether perioperative multimodal warming would reduce the incidence of POCD and improve prognosis in elderly patients with gynaecological cancer.
Methods And Analysis: This is a single-centre, prospective, single-blinded randomised controlled trial. One hundred and fifty patients for gynaecological cancer surgery and 16 non-surgical controls aged 65 years or older will be studied in this trial. A series of neuropsychological tests will be completed to evaluate cognitive function in surgery patients before, at day 7 and 3 months after gynaecological cancer surgery. In addition, POCD and cognitive decline will be assessed using the reliable change index using the control group's results. The primary outcome is the prevalence of POCD in elderly gynaecological cancer surgery patients and association between perioperative multimodal warming and POCD.
Ethics And Dissemination: The protocol for this prospective observational study was approved by the ethics committee of the West China Second University Hospital, Sichuan University (NO. KX215). Recruitment will commence in April 2021 and continue to April 2022. The findings of this trial will be disseminated in peer-reviewed journals and scientific meetings.
Trial Registration Number: ChiCTR2100041663.
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http://dx.doi.org/10.1136/bmjopen-2021-049186 | DOI Listing |
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Of the total carbon footprint of Australia, 7% is attributed to healthcare. In the UK, inhalational agents make up 5% of the healthcare carbon footprint. This systematic review aims to determine which methods of education about the environmental impact of inhalational anaesthetic agents can be utilised to promote behaviour change, reducing the anaesthetic-related carbon footprint.
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The rise of the metaverse and the increasing volume of heterogeneous 2D and 3D data have created a growing demand for cross-modal retrieval, enabling users to query semantically relevant data across different modalities. Existing methods heavily rely on class labels to bridge semantic correlations; however, collecting large-scale, well-labeled data is expensive and often impractical, making unsupervised learning more attractive and feasible. Nonetheless, unsupervised cross-modal learning faces challenges in bridging semantic correlations due to the lack of label information, leading to unreliable discrimination.
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