A machine learning-based approach for precision risk stratification and multifactorial analysis of needlestick injuries in oral and maxillofacial surgery nursing
Abstract Background Needlestick 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 techn...
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| Language: | English |
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BMC
2025-07-01
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| Series: | BMC Nursing |
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| Online Access: | https://doi.org/10.1186/s12912-025-03362-9 |
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| author | Xiulan Deng Jiayang Han Linlin Yin Yongzhi Pang Qingbin Han Lu Zhao Wenlei Liu Qing Li |
| author_facet | Xiulan Deng Jiayang Han Linlin Yin Yongzhi Pang Qingbin Han Lu Zhao Wenlei Liu Qing Li |
| author_sort | Xiulan Deng |
| collection | DOAJ |
| description | Abstract Background Needlestick 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. Methods A 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. Results The 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). Conclusions These 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. Trial registration Not applicable. |
| format | Article |
| id | doaj-art-e6ab25bbcd6f4b6f9179c3d04ba35fd7 |
| institution | DOAJ |
| issn | 1472-6955 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Nursing |
| spelling | doaj-art-e6ab25bbcd6f4b6f9179c3d04ba35fd72025-08-20T03:03:34ZengBMCBMC Nursing1472-69552025-07-012411910.1186/s12912-025-03362-9A machine learning-based approach for precision risk stratification and multifactorial analysis of needlestick injuries in oral and maxillofacial surgery nursingXiulan Deng0Jiayang Han1Linlin Yin2Yongzhi Pang3Qingbin Han4Lu Zhao5Wenlei Liu6Qing Li7Shandong Provincial Hospital Affiliated to Shandong First Medical UniversitySchool and Hospital of Stomatology, Cheeloo College of Medicine, Shandong UniversityJinan Keen Dental HospitalJinan Dental HospitalDepartment of Oral and Maxillofacial Surgery, Linyi People’s HospitalDepartment of Oral and Maxillofacial Surgery, Binzhou People’s HospitalSchool and Hospital of Stomatology, Cheeloo College of Medicine, Shandong UniversitySchool and Hospital of Stomatology, Cheeloo College of Medicine, Shandong UniversityAbstract Background Needlestick 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. Methods A 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. Results The 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). Conclusions These 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. Trial registration Not applicable.https://doi.org/10.1186/s12912-025-03362-9Needlestick injuriesRisk assessmentMachine learningOral and maxillofacial surgery |
| spellingShingle | Xiulan Deng Jiayang Han Linlin Yin Yongzhi Pang Qingbin Han Lu Zhao Wenlei Liu Qing Li A machine learning-based approach for precision risk stratification and multifactorial analysis of needlestick injuries in oral and maxillofacial surgery nursing BMC Nursing Needlestick injuries Risk assessment Machine learning Oral and maxillofacial surgery |
| title | A machine learning-based approach for precision risk stratification and multifactorial analysis of needlestick injuries in oral and maxillofacial surgery nursing |
| title_full | A machine learning-based approach for precision risk stratification and multifactorial analysis of needlestick injuries in oral and maxillofacial surgery nursing |
| title_fullStr | A machine learning-based approach for precision risk stratification and multifactorial analysis of needlestick injuries in oral and maxillofacial surgery nursing |
| title_full_unstemmed | A machine learning-based approach for precision risk stratification and multifactorial analysis of needlestick injuries in oral and maxillofacial surgery nursing |
| title_short | A machine learning-based approach for precision risk stratification and multifactorial analysis of needlestick injuries in oral and maxillofacial surgery nursing |
| title_sort | machine learning based approach for precision risk stratification and multifactorial analysis of needlestick injuries in oral and maxillofacial surgery nursing |
| topic | Needlestick injuries Risk assessment Machine learning Oral and maxillofacial surgery |
| url | https://doi.org/10.1186/s12912-025-03362-9 |
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