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To carry out the translation and cultural adaptation of the attitude towards pressure ulcer prevention instrument for use in Chinese and to analyse the validity and reliability of the adapted version of the questionnaire. In this quantitative, descriptive, cross-sectional study, after translation of the questionnaire from English to Chinese, back-translation, and assessment of equivalence between the original and back-translated version by an expert panel, the Chinese version instrument was assessed by a convenience sample of registered nurses in several hospitals in cities of China. The internal consistency and content validity of the instrument was tested, and a confirmatory factor analysis was also performed. Confirmatory factor analysis showed that the goodness of fit of the five-factor model after the scale localization was not ideal. Therefore, confirmatory factor analysis is performed to obtain the threefactor solution of comparative fit index, goodness-of-fit index, and adjusted goodness-of-fit index reaching the acceptable standard. The instrument score of nurses with wound care certification was significantly higher than that of nurses without wound care special certification. The adapted version of the instrument for Chinese nurses can be used as a tool to measure attitudes towards pressure injury prevention. K E Y W O R D S attitude, nurses, pressure ulcer, prevention, scale development Key Messages • based on the attitude towards pressure ulcer prevention (APuP) instrument developed by Beeckman and his colleagues, this study developed a Chinese version of the instrument to provide tool support for the survey of pressure ulcer prevention attitudes among Chinese nurses Xuemiao Huang and Tao Zan contributed equally to this work.

Background: Graft-versus-host disease (GVHD) is a fatal complication of hematopoietic stem cell transplantation and is an enormous burden on the patient economy and related health systems. Nevertheless, only a few bibliometric studies have examined the direction of research and the major findings within the field. Methods: Statistical and visualization bibliometric analysis was performed in April 2021. Our research data were retrieved from the Web of Science using an advanced search strategy. We then used bibliometric analysis to determine the current general research direction and trend of publications and established the most prolific and distinguished authors, institutions, countries, funding agencies, and keywords in GVHD research. We employed VOSviewer (Leiden University, Leiden, Netherlands), Microsoft Excel (Microsoft, Redmond, State of Washington), and GunnMap (https://lert.co.nz/map/) to retrieve, integrate, and visualize the results. Results: Overall, 15,378 publications from 500 journals were extracted from the Institute for Scientific Information (ISI) Web of Science Core Collection database based on our analysis, of which the United States and the Fred Hutchinson Cancer Research Center were the most prolific countries and institutions, respectively. Moreover, we identified future research trends and the current status of GVHD research based on the top 10 most cited articles. Finally, influential authors’ analysis demonstrated that Blazar, BR were both the most productive and most cited among all authors. Conclusion: Our study provides an exhaustive and objective overview of the current status of GVHD research. This information would be highly beneficial to anyone seeking information on GVHD and would serve as a reference guide for researchers aiming to conduct further GVHD research.

Objective This review aimed to summarize the use of machine learning for predicting the potential benefits of stroke rehabilitation treatments, to evaluate the risk of bias of predictive models, and to provide recommendations for future models. Materials and methods This systematic review was conducted in accordance with the PRISMA statement and the CHARMS checklist. The PubMed, Embase, Cochrane Library, Scopus, and CNKI databases were searched up to April 08, 2023. The PROBAST tool was used to assess the risk of bias of the included models. Results Ten studies within 32 models met our inclusion criteria. The optimal AUC value of the included models ranged from 0.63 to 0.91, and the optimal R2 value ranged from 0.64 to 0.91. All of the included models were rated as having a high or unclear risk of bias, and most of them were downgraded due to inappropriate data sources or analysis processes. Discussion and conclusion There remains much room for improvement in future modeling studies, such as high-quality data sources and model analysis. Reliable predictive models should be developed to improve the efficacy of rehabilitation treatment by clinicians.

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