ArticleBMC nursing2025
A machine learning-based approach for precision risk stratification and multifactorial analysis of needlestick injuries in oral and maxillofacial surgery nursing.
Article in BMC nursing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundNeedlestick injuries are a significant occupational hazard for oral and maxillofacial surgery nurses. This hazard results from complex procedures, limited workspace, and frequent handling of sharp instruments. This study uses advanced clustering and dimensionality reduction techniques to identify high-risk groups and key contributing factors.
methodsA structured questionnaire was administered to 224 nurses in five hospitals in Shandong Province. Spearman correlation analysis was used to identify critical risk variables, while K-means clustering and t-SNE visualization were used for risk stratification.
resultsThe study results showed that 33.93% of the nurses were classified as high risk, 35.27% as medium risk, and 30.8% as low risk. Analysis revealed that nurses in the high-risk category experienced significantly poor working conditions and suboptimal instrument management (P < 0.001), as well as lower levels of patient cooperation and more challenging surgical environments (P < 0.001).
conclusionsThese findings underscore the urgent need for data-driven, targeted interventions to improve the surgical environment, optimize instrument management, and enhance patient cooperation, providing a robust framework for reducing needlestick injuries in oral and maxillofacial surgical care.
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