OBJECTIVE: To analyze the prevalence and occupational risk factors of low back pain in nurses. METHODS: A random sampling method was used to select 648 female nurses from 8 hospitals as study subjects. The self-designed Questionnaire on Low Back Pain in Nurses was used to investigate the conditions of low back pain and analyze its occupational risk factors. RESULTS: The prevalence rate of low back pain in nurses was 63.0%(408/648). Among them, 28.2%(115/408) of nurses with low back pain had a definite diagnosis of lumbar disc herniation and 90.7%(370/408) developed low back pain after nursing work. The multivariate logistic analysis showed that the nurses with younger age, shorter length of service, higher body mass index and higher frequency of lifting heavy objects, had the higher risk of low back pain(P<0.05). The nurses who rotated their trunk, carried patients, and assisted bedridden patients to change their positions during nursing operations had a relatively higher risk of low back pain(P<0.01). CONCLUSION: The length of service and poor working posture are the occupational risk factors of low back pain in nurses.

Objective To explore the application effect of web-based model of Just-in-Time-Teaching (JiTT) in nursing courses in higher vocational education.Methods A total of 362 nursing students enrolled in 2014 to Nanyang Medical College were selected as the observation group, and the 397 nursing students enrolled in 2013 as the control group. In the course of Fundamental Nursing, offered in the first semester in the second year, students in the control group were taught with traditional teaching method, while those in the experimental group with model JiTT. Performance of the students between the two groups was compared in terms of theoretical exam and skill evaluation, and their autonomous learning ability.Results In the course Fundamental Nursing, scores of theoretical exam (85.66±3.23), skill evaluation (88.43±3.92), and total scores of autonomous learning (101.47±9.33) in the observation group were all higher than those in the control group (t=5.574, 5.626, 5.427;P<0.01).Conclusions The model JiTT can improve autonomous learning ability and teaching efficiency, which makes it a positive reference for use of modern information technology in nursing courses in higher vocational education.

Objective To explore the effects of assessment system based on key points in clinical skills training among nursing interns.Methods Nursing interns of the Affiliated Cancer Hospital of Zhengzhou University were selected as study subjects from January to December 2016 by the method of convenience sampling. They were divided into experimental group (n=50, practice started from January 2016) and control group (n=50, practice started from June 2016) according to the order of internship. Students in control group adopted the conventional skill training and assessment, while students in experimental group received the assessment system based on key points. Critical thinking skills of nursing interns of both groups were compared with the critical thinking disposition inventory (CTDI).Results After training, the total score of CTDI of students (299.82±26.95) in experimental group was higher than that (283.41±24.24) in control group with a significant difference (t=3.201,P=0.002). The scores of 7 dimensions in CTDI were (41.18±4.12), (40.27±3.16), (46.10±2.00), (40.11±5.31), (42.97±5.05), (43.96±3.88), (45.23±3.08) significantly higher than those in control group[(39.62±3.09), (38.62±4.15), (42.04±2.95), (37.92±3.65), (39.50±3.57), (41.82±5.17), (43.51±2.64)] (t=2.142, 2.237, 8.074, 2.402, 3.967, 2.341, 2.981;P<0.05).Conclusions The assessment system based on key points can effectively improve the critical thinking skills of nursing interns. It is worth to be used widely.

Objective:To investigate the self-measured health status of college students in Tibet, and to construct the self-measured health scale (SRHMS V1.0) norm of College students in Tibet.Methods:This was a cross-sectional study. A multistage stratified sampling method was used. From April to June 2022, a total of 7 990 college students were selected from all colleges and universities in the Tibet Autonomous Region (7 in total). The self-rated health of Tibetan college students was investigated and evaluated by combining demographic information and SRHMS V1.0. Descriptive statistics, t-test, analysis of variance ( Brown-Forsythe test for unequal variance, LSD test for multiple comparisons), and Spearman correlation analysis were used to construct mean norm, percentile norm, and delimitation norm of physiological scale health (PSH), mental scale health (MSH), social scale health (SSH) score and total scale health (TSH) scores. Results:The total score of self-assessed health assessment among college students in Tibetan universities was (72.18±12.35). For different genders, the PSH, MSH, SSH and TSH scores were (73.85±13.78), (65.80±14.73), (69.85±16.00) and (73.44±12.77) for boys and (71.18±13.36), (62.81±14.03), (68.57±14.90) and (70.92±11.94) for girls, respectively. Scores on each subscale and total scale were statistically significant different between the different sexes ( t=2.531, 2.672, 1.867, 2.623, all P<0.05). For different grades, the PSH, MSH, SSH and TSH scores of the freshman were (73.36±13.23), (65.77±14.58), (70.98±15.60) and (73.51±11.91); the sophomore were (70.74±13.73), (62.40±13.60), (66.92±14.62) and (70.16±12.28), the junior were (75.48±13.09), (64.08±15.12), (71.90±15.12) and (74.10±12.36); and the senior were (67.21±14.41), (59.19±17.67), (64.91±18.59) and (66.94±14.59) respectively, with the differences in scores of each subscale and the total scale in different grades being statistically significant ( F=3.952, 3.611, 4.841, 5.583, all P<0.05). The mean norm, percentile norm and demarcation norm of the total score and each subscale of self-measured health of college students in Tibetan universities were constructed with gender and grade as the cut-off values. Conclusion:The model of self-measured health assessment scale for students in colleges and universities in Tibet is established, which can provide evaluation criteria for evaluating the health status of college students in Tibet and plateau areas.

Objective To explore a method to improve the accuracy and generalization ability of medical diagnostic neural network models under conditions of small data volumes,and to address the issue of poor neural network model performance in computer-aided diagnosis of nail diseases due to limited training data.Methods A dual-model strategy integrating instance segmentation with fine-grained feature classification was proposed.The neural network model based on dual-model strategy was trained using the dataset of Image-Based Intelligent Diagnosis of Nail Disease Model task of the first National Digital Health Innovation Application Competition & Health and Medical Big Data Theme Competition.This dataset covered 8 types of nail diseases,including nail matrix nevi,paronychia,nail psoriasis,onychomycosis,subungual hemorrhage,melanonychia,periungual warts,and nail melanoma,with class imbalance present.The diagnostic performance of the dual-model strategy was evaluated and compared with single-model strategies(image classification models[ResNet50 and Swin Transformer]and target detection model based on faster region-based convolutional neural network[Faster R-CNN])under the same hardware and software training conditions.Results The dataset included 1 048 samples,including 210 cases of nail matrix nevi,186 cases of paronychia,69 cases of nail psoriasis,203 cases of onychomycosis,149 cases of subungual hemorrhage,71 cases of melanonychia,93 cases of periungual warts,and 67 cases of nail melanoma,with 90％used for training various models and 10％for evaluation.The micro F1 score was 0.324 in the image classification model based on ResNet50,0.381 in the image classification model based on Swin Transformer,0.572 in the target detection model based on Faster R-CNN,and 0.714 in the dual-model strategy model Mask R-CNN+Swin Transformer.The accuracy rates for diagnosing different nail diseases in the dual-model strategy were:nail matrix nevi 80.95％(17/21),paronychia 89.47％(17/19),nail psoriasis 100.00％(7/7),onychomycosis 70.00％(14/20),subungual hemorrhage 73.33％(11/15),melanonychia 14.29％(1/7),periungual warts 55.56％(5/9),and nail melanoma 42.86％(3/7).The micro F1 score for evaluating the dual-model strategy on a test set of 1 000 cases was 0.844.Conclusion The dual-model strategy can effectively combine models with different functions to well accomplish the task of intelligent diagnosis of nail diseases under small data volume training conditions.

Objective:To assess the diagnostic efficacy of upper gastrointestinal endoscopic image assisted diagnosis system (ENDOANGEL-LD) based on artificial intelligence (AI) for detecting gastric lesions and neoplastic lesions under white light endoscopy.Methods:The diagnostic efficacy of ENDOANGEL-LD was tested using image testing dataset and video testing dataset, respectively. The image testing dataset included 300 images of gastric neoplastic lesions, 505 images of non-neoplastic lesions and 990 images of normal stomach of 191 patients in Renmin Hospital of Wuhan University from June 2019 to September 2019. Video testing dataset was from 83 videos (38 gastric neoplastic lesions and 45 non-neoplastic lesions) of 78 patients in Renmin Hospital of Wuhan University from November 2020 to April 2021. The accuracy, the sensitivity and the specificity of ENDOANGEL-LD for image testing dataset were calculated. The accuracy, the sensitivity and the specificity of ENDOANGEL-LD in video testing dataset for gastric neoplastic lesions were compared with those of four senior endoscopists.Results:In the image testing dataset, the accuracy, the sensitivity, the specificity of ENDOANGEL-LD for gastric lesions were 93.9% (1 685/1 795), 98.0% (789/805) and 90.5% (896/990) respectively; while the accuracy, the sensitivity and the specificity of ENDOANGEL-LD for gastric neoplastic lesions were 88.7% (714/805), 91.0% (273/300) and 87.3% (441/505) respectively. In the video testing dataset, the sensitivity [100.0% (38/38) VS 85.5% (130/152), χ2=6.220, P=0.013] of ENDOANGEL-LD was higher than that of four senior endoscopists. The accuracy [81.9% (68/83) VS 72.0% (239/332), χ2=3.408, P=0.065] and the specificity [ 66.7% (30/45) VS 60.6% (109/180), χ2=0.569, P=0.451] of ENDOANGEL-LD were comparable with those of four senior endoscopists. Conclusion:The ENDOANGEL-LD can accurately detect gastric lesions and further diagnose neoplastic lesions to help endoscopists in clinical work.

Objective:To construct a real-time artificial intelligence (AI)-assisted endoscepic diagnosis system based on YOLO v3 algorithm, and to evaluate its ability of detecting focal gastric lesions in gastroscopy.Methods:A total of 5 488 white light gastroscopic images (2 733 images with gastric focal lesions and 2 755 images without gastric focal lesions) from June to November 2019 and videos of 92 cases (288 168 clear stomach frames) from May to June 2020 at the Digestive Endoscopy Center of Renmin Hospital of Wuhan University were retrospectively collected for AI System test. A total of 3 997 prospective consecutive patients undergoing gastroscopy at the Digestive Endoscopy Center of Renmin Hospital of Wuhan University from July 6, 2020 to November 27, 2020 and May 6, 2021 to August 2, 2021 were enrolled to assess the clinical applicability of AI System. When AI System recognized an abnormal lesion, it marked the lesion with a blue box as a warning. The ability to identify focal gastric lesions and the frequency and causes of false positives and false negatives of AI System were statistically analyzed.Results:In the image test set, the accuracy, the sensitivity, the specificity, the positive predictive value and the negative predictive value of AI System were 92.3% (5 064/5 488), 95.0% (2 597/2 733), 89.5% (2 467/ 2 755), 90.0% (2 597/2 885) and 94.8% (2 467/2 603), respectively. In the video test set, the accuracy, the sensitivity, the specificity, the positive predictive value and the negative predictive value of AI System were 95.4% (274 792/288 168), 95.2% (109 727/115 287), 95.5% (165 065/172 881), 93.4% (109 727/117 543) and 96.7% (165 065/170 625), respectively. In clinical application, the detection rate of local gastric lesions by AI System was 93.0% (6 830/7 344). A total of 514 focal gastric lesions were missed by AI System. The main reasons were punctate erosions (48.8%, 251/514), diminutive xanthomas (22.8%, 117/514) and diminutive polyps (21.4%, 110/514). The mean number of false positives per gastroscopy was 2 (1, 4), most of which were due to normal mucosa folds (50.2%, 5 635/11 225), bubbles and mucus (35.0%, 3 928/11 225), and liquid deposited in the fundus (9.1%, 1 021/11 225).Conclusion:The application of AI System can increase the detection rate of focal gastric lesions.