With the rapid rise of large language models(LLM),the natural language generation capabilities of deep learning have demonstrated significant value in the medical field.However,the"closed nature"of model parameters makes them prone to generating"hallucinations",making it difficult to provide accurate answers to the latest knowledge,and the reasoning process lacks transparency and traceability.Retrieval-augmented generation(RAG)technology addresses these issues by actively connecting external information sources such as document databases and knowledge graphs during the generation process.This significantly reduces the dependence of LLM on outdated training data and introduces verifiable evidence and real-time knowledge updates into their responses.In the medical field,RAG technology effectively addresses the high-accuracy and traceability requirements of literature retrieval and clinical decision support.It is widely applied in areas such as drug discovery,pharmacovigilance,and the diagnosis and treatment of rare diseases.By integrating emerging technologies such as reinforcement learning,multimodal processing,and compliant privacy protection,RAG technology is evolving towards a more open and highly customizable direction,providing innovative intelligent solutions for medical information retrieval and decision-making support.

With the rapid rise of large language models(LLM),the natural language generation capabilities of deep learning have demonstrated significant value in the medical field.However,the"closed nature"of model parameters makes them prone to generating"hallucinations",making it difficult to provide accurate answers to the latest knowledge,and the reasoning process lacks transparency and traceability.Retrieval-augmented generation(RAG)technology addresses these issues by actively connecting external information sources such as document databases and knowledge graphs during the generation process.This significantly reduces the dependence of LLM on outdated training data and introduces verifiable evidence and real-time knowledge updates into their responses.In the medical field,RAG technology effectively addresses the high-accuracy and traceability requirements of literature retrieval and clinical decision support.It is widely applied in areas such as drug discovery,pharmacovigilance,and the diagnosis and treatment of rare diseases.By integrating emerging technologies such as reinforcement learning,multimodal processing,and compliant privacy protection,RAG technology is evolving towards a more open and highly customizable direction,providing innovative intelligent solutions for medical information retrieval and decision-making support.

OBJECTIVE: To investigate the prevalence and risk factors of multi-site work-related musculoskeletal disorders(WMSDs) among workers in the industry of electronic equipment manufacturing. METHODS: A total of 815 workers in three factories of electronic equipment manufacturing in Guangdong Province were selected as study subjects by convenience sampling. The prevalence of multi-site WMSDs in the past year was investigated using Musculoskeletal Disorders Investigating Questionnaire and the influencing factors were analyzed. RESULTS: The total prevalence of WMSDs was 69.4%(566/815). The prevalence of multi-site WMSDs was 54.5%(444/815), and the prevalence of one-site WMSDs was 15.0%(122/815). Multiple logistic regression showed that female workers had higher prevalence of multi-site WMSDs than males [odds radio(OR) and 95% confidence interval(CI): 1.59(1.12-2.26), P<0.05]. The prevalence of multi-site WMSDs in left-handed workers was lower than that of right-handed workers [OR(95% CI): 0.42(0.19-0.91), P<0.05]. The longer service of current position and the more neck forward movement, the higher prevalence of multi-site WMSDs [OR(95% CI) were 1.33(1.09-1.63) and 1.62(1.23-2.15), P<0.01]. The workers who had long-time sitting at work, adopted uncomfortable working posture, could decide when to work on their own, kept head down for a long time, or often bending wrists up/down had higher prevalence of multi-site WMSDs [OR(95% CI) were 1.41(1.16-1.73), 1.82(1.40-2.38), 1.79(1.16-2.75), 1.92(1.38-2.69) and 1.60(1.14-2.24), respectively, P<0.01]. The workers who could take turns with colleagues to finish work or had enough rest time had lower prevalence of multi-site WMSDs [OR(95% CI): 0.57(0.41-0.78) and 0.67(0.48-0.92), P<0.05]. The workers who worked >10 h per day had lower prevalence of multi-site WMSDs than those who worked ≤8 h per day [OR(95% CI): 0.57(0.37-0.87), P<0.05]. CONCLUSION: Multi-site WMSDs were more common than one-site WMSDs among workers in the industry of electronic equipment manufacturing, and the prevalence of multi-site WMSDs was high. The risk factors include personal factors, work organization and adverse ergonomic factors.