Objective To explore the current status of preoperative hope level and its influencing factors in scoliosis patients, focusing on the role of medical coping, social support and self-care ability on the hope level, and to provide a basis for optimising perioperative psychological interventions. Methods Preoperative scoliosis patients at West China Hospital of Sichuan University between January 2024 and January 2025 were selected. Patients were included in the survey using a general information questionnaire, Herth Hope Index (HHI), Medical Coping Questionnaire, Social Support Rating Scale (SSRS), and Daily Living Ability Scale. Multiple linear regression analyses were performed and influential factors were explored with HHI score as the dependent variable. Results A total of 156 patients were investigated. Among them, there were 104 females (66.67%); The average HHI score was (36.88±4.04) points; 41.03% (64 cases) of patients were at a low to moderate hope level (HHI≤35 points). There were statistically significant differences in HHI scores among patients with different marital statuses and disease durations (P<0.05). The correlation analysis results showed that social support was positively correlated with HHI (r=0.207, P=0.010); Medical coping (r=?0.015, P=0.852) and self-care ability (r=0.010, P=0.903) were not correlated with HHI. The results of multiple linear regression analysis showed that the total SSRS score affected the HHI score of preoperative scoliosis patients (P=0.040). Conclusion Multidisciplinary interventions should be implemented for patients with low levels of hope, focusing on married patients with a disease duration of 1-5 years, and improving their level of hope by strengthening the social support network.
Objective To develop a computer-aided diagnosis model for lung cancer based on routine health examination data for identifying individuals with a current high risk of lung cancer in health screening settings, thereby providing decision support for subsequent clinical confirmation. Methods Individuals who underwent health examinations at the Health Management Center of West China Hospital, Sichuan University, between 2010 and 2022 were enrolled. After screening, a retrospective cohort of 5257 subjects was retained, comprising 1307 patients with lung cancer and 3950 non-lung cancer controls. A three-tier feature fusion model was designed: Heterogeneous feature encoding module: a multi-layer perceptron and bidirectional encoder representations from transformers (BERT) were employed to extract feature vectors from structured data and unstructured data (medical records and imaging report texts), respectively. Heterogeneous feature fusion architecture: dimensional expansion concatenation coupled with a gated recurrent unit based gating network was implemented to achieve multi-scale feature alignment and deep interaction, thereby addressing dimensional discrepancies and information redundancy. Attention-based decision mechanism: word-level attention with weighted pooling was applied to dynamically capture key features and generate risk probability distributions. Model performance was evaluated using precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC-ROC). Results The proposed model significantly outperformed both single-data-type models and simple concatenation approaches. On the test set, the proposed model achieved a recall of 0.861, an F1-score of 0.882, and an AUC-ROC of 0.972, substantially surpassing the best-performing model trained on structured data alone (extreme gradient boosting: recall=0.630, F1-score=0.725, AUC-ROC=0.916) and the model trained on unstructured data alone (BERT coupled with a bidirectional long short-term memory network: recall=0.833, F1-score=0.846, AUC-ROC=0.944). Feature elimination experiments demonstrated minimal performance variation across different feature subsets, confirming the model’s capability to effectively identify and mitigate the impact of irrelevant features. Subgroup analyses revealed that the model performed optimally in female subjects (recall=0.835, F1-score=0.838, AUC-ROC=0.950) and individuals aged >69 years (recall=0.913, F1-score=0.875, AUC-ROC=0.911). Conclusion The proposed model based on heterogeneous health examination data can identify high-risk individuals for lung cancer among health examination populations using only routine screening data, thereby facilitating the early diagnosis of lung cancer in this population.
In post-coronavirus disease 2019 era, people’s style of work and life have undergone major changes. The sedentary style of work and life, such as long-time office work, online meetings, home eating, online social interactions, and reduced range of activities, affect people’s physical and mental health. Neck and shoulder pain is one of the common symptoms. By combining the clinical practice experience of orthopedic medical experts in West China Hospital of Sichuan University, and reviewing a large number of literatures, this article summarized the definition, incidence, hazards, causes, evaluation and prevention of neck and shoulder pain in post-coronavirus disease 2019 era. It aimed to provide experience for the prevention and treatment of neck and shoulder pain in post-coronavirus disease 2019 era.
