Objective:To explore the application effect of Internet+ stratified teaching based on ClassIn platform in standardized training for neurology nurses.Methods:A total of 80 nurses who underwent standardized training between July 2022 and January 2024 were randomly divided into control group (traditional teaching) and observation group (Internet+ stratified teaching), with 40 participants in each group. In the observation group, 3D stratification was performed using the ClassIn platform. Specifically, layer A (extension group) received interdisciplinary PBL and VR advanced training (e.g., cerebrovascular intervention simulation), layer B (standard group) received personalized training package generated by an intelligent diagnosis system, and layer C (basic group) received stepped coding micro-class teaching. The control group received centralized lectures plus simulated man exercise. The differences in theoretical assessments, operational skills, clinical practice abilities, and comprehensive quality between the two groups were compared. SPSS 27.0 was used for t test and χ2 test. Results:The total theoretical assessment score of nurses in the observation group was higher than that in the control group, particularly in case analysis [neurology case analysis: (8.42±0.24) vs. (5.80±1.20)]. In operational skill assessment, the scores of GCS [(91.83±5.01) vs. (72.42±9.31)] and CPR [(93.41±4.90) vs. (73.60±9.12)] in the observation group were higher than those in the control group. In terms of clinical practice, nurses in the observation group demonstrated superior performance in condition observation ability [(8.50±1.10) vs. (5.80±1.70)] and health education [(9.20±0.70) vs. (6.50±1.30)]. In terms of the quality of humanistic care, the observation group outperformed the control group in complete rate of interpretation before operation [(8.61±0.54) vs. (6.67±1.12)], response rate of non-verbal demand [(8.43±0.37) vs. (6.15±1.02)], and rate of family members mastering nursing skills [(8.35±0.41) vs. (6.31±1.27)]. The comprehensive quality evaluation showed that team cooperation [(4.90±0.30) vs. (3.50±1.10)] and emergency response ability [(4.50±0.60) vs. (3.20±1.20)] were significantly improved in the observation group.Conclusions:The model of Internet+ stratified teaching based on ClassIn platform can effectively improve theoretical knowledge, operational standardization, and clinical comprehensive ability of neurology nurses. This approach emphasizes humanistic care in nursing. The intelligent stratification and dynamic regulation mechanism provide new ideas for nursing training.

Objective:To explore the application effect of Internet+ stratified teaching based on ClassIn platform in standardized training for neurology nurses.Methods:A total of 80 nurses who underwent standardized training between July 2022 and January 2024 were randomly divided into control group (traditional teaching) and observation group (Internet+ stratified teaching), with 40 participants in each group. In the observation group, 3D stratification was performed using the ClassIn platform. Specifically, layer A (extension group) received interdisciplinary PBL and VR advanced training (e.g., cerebrovascular intervention simulation), layer B (standard group) received personalized training package generated by an intelligent diagnosis system, and layer C (basic group) received stepped coding micro-class teaching. The control group received centralized lectures plus simulated man exercise. The differences in theoretical assessments, operational skills, clinical practice abilities, and comprehensive quality between the two groups were compared. SPSS 27.0 was used for t test and χ2 test. Results:The total theoretical assessment score of nurses in the observation group was higher than that in the control group, particularly in case analysis [neurology case analysis: (8.42±0.24) vs. (5.80±1.20)]. In operational skill assessment, the scores of GCS [(91.83±5.01) vs. (72.42±9.31)] and CPR [(93.41±4.90) vs. (73.60±9.12)] in the observation group were higher than those in the control group. In terms of clinical practice, nurses in the observation group demonstrated superior performance in condition observation ability [(8.50±1.10) vs. (5.80±1.70)] and health education [(9.20±0.70) vs. (6.50±1.30)]. In terms of the quality of humanistic care, the observation group outperformed the control group in complete rate of interpretation before operation [(8.61±0.54) vs. (6.67±1.12)], response rate of non-verbal demand [(8.43±0.37) vs. (6.15±1.02)], and rate of family members mastering nursing skills [(8.35±0.41) vs. (6.31±1.27)]. The comprehensive quality evaluation showed that team cooperation [(4.90±0.30) vs. (3.50±1.10)] and emergency response ability [(4.50±0.60) vs. (3.20±1.20)] were significantly improved in the observation group.Conclusions:The model of Internet+ stratified teaching based on ClassIn platform can effectively improve theoretical knowledge, operational standardization, and clinical comprehensive ability of neurology nurses. This approach emphasizes humanistic care in nursing. The intelligent stratification and dynamic regulation mechanism provide new ideas for nursing training.

Objective To evaluate the nucleated red blood cell (NRBC)count of Sysmex XN-9000 automatic hematology anal-ysis lines comparing with manual method,and verify the accuracy of the analyzer results.Methods 60 blood samples with more than 1% of NRBCs detected by XN-9000 were counted NRBCs by traditional manual microscopy in blood smears,and verified the analyzer results.Results According to the reliability analysis,the results of total 60 samples were all within the range of the reliability;correlation analysis showed that correlation coefficient (r)of group NRBC (%)1~10 and>10 were 0.972 1 and 0.996 2,respectively.There were no significant differences between them (P>0.05).Conclusion Compared with manual method,the results of NRBC count of XN-9000 were within the range of reliability,and showed good correla-tion.The analyzer test results of NRBC were accurate and reliable and could be applied to the detection of clinical samples.