Objective: To analyze the ear, nose, and throat exam of some freshmen in the military college entrance examination in Shandong Province in 2020 and to facilitate adolescent targeted health promotion. Methods: The 1 411 freshmen participating in the military college entrance examination in Jinan, Zibo and Weifang of Shandong Province were included. The ear, nose, and throat exam were performed by professionals using electric otoscope, 5 meter whispering test, and front rhinoscope. Results: Nasal septal deviation and hypertrophy of inferior turbinate accounted for the highest proportion. Among 489 cases of nasal septum deviation, the detection rate of Jinan (15.97%) was significantly lower than that of Weifang (43.60%) and Zibo (46.53%) ( χ 2=63.32， P <0.05). For deviation of nasal septum, the detection rate in students with urban residence (31.53%) was significantly lower than that of rural students (39.03%) ( χ 2=4.11， P <0.05). Seventy two cases of inferior turbinate hyperplasia were detected, and the detection rate in Jinan (2.99%) was significantly lower than that in Weifang (6.51%) and Zibo (6.04%) ( χ 2=6.63， P <0.05). The detection rate of tonsil hypertrophy was significantly lower in boys (4.63%), students from urban area (3.94%), compared with that of girls(9.56%) and rural students (6.70%) ( χ 2=5.35，4.86， P <0.05). In pharyngeal examination, tonsil hyperplasia was the most common condition of enlarged tonsils ( n =214), which was significantly higher in Jinan(22.36%) than that of Weifang (11.71 %) and Zibo (10.74%) ( χ 2=22.39， P <0.05), and was significantly lower in boys (14.38%) and rural students (12.40%) than that in girls (22.79%) and urban students (17.24%) ( χ 2=4.70，4.65， P <0.05). Conclusion Nasal septum deviation and tonsil hypertrophy are the most prevalent upper airway diseases among freshmen participating in the military college entrance examination. Prevention and treatment of nasopharynx diseases should be emphasized.

ObjectiveTo construct a large language model （LLM）-based intelligent pre-consultation system for traditional Chinese medicine （TCM） to improve efficacy of clinical practice. MethodsA TCM large language model was fine-tuned using DeepSpeed ZeRO-3 distributed training strategy based on YAYI 2-30B. A weighted undirected graph network was designed and an agent-based syndrome differentiation model was established based on relationship data extracted from TCM literature and clinical records. An agent collaboration framework was developed to integrate the TCM LLM with the syndrome differentiation model. Model performance was comprehensively evaluated by Loss function， BLEU-4， and ROUGE-L metrics， through which training convergence， text generation quality， and language understanding capability were assessed. Professional knowledge test sets were developed to evaluate system proficiency in TCM physician licensure content， TCM pharmacist licensure content， TCM symptom terminology recognition， and meridian identification. Clinical tests were conducted to compare the system with attending physicians in terms of diagnostic accuracy， consultation rounds， and consultation duration. ResultsAfter 100 000 iterations， the training loss value was gradually stabilized at about 0.7±0.08， indicating that the TCM-LLM has been trained and has good generalization ability. The TCM-LLM scored 0.38 in BLEU-4 and 0.62 in ROUGE-L， suggesting that its natural language processing ability meets the standard. We obtained 2715 symptom terms， 505 relationships between diseases and syndromes， 1011 relationships between diseases and main symptoms， and 1 303 600 relationships among different symptoms， and constructed the Agent of syndrome differentiation model. The accuracy rates in the simulated tests for TCM practitioners， licensed pharmacists of Chinese materia medica， recognition of TCM symptom terminology， and meridian recognition were 94.09%， 78.00%， 87.50%， and 68.80%， respectively. In clinical tests， the syndrome differentiation accuracy of the system reached 88.33%， with fewer consultation rounds and shorter consultation time compared to the attending physicians （P＜0.01）， suggesting that the system has a certain pre- consultation ability. ConclusionThe LLM-based intelligent TCM pre-diagnosis system could simulate diagnostic thinking of TCM physicians to a certain extent. After understanding the patients' natural language， it collects all the patient's symptom through guided questioning， thereby enhancing the diagnostic and treatment efficiency of physicians as well as the consultation experience of the patients.