Nomogram reliability for predicting potential risk in postgraduate medical students with anxiety symptoms

Enhancing smart healthcare with female students' stress and anxiety detection using machine learning

Machine learning (ML) is widely used to predict and detect stress and anxiety. Early detection of stress or anxiety is crucial for clinical pathways to enhance the supportive environment in society, particularly among female students. This study aims to assess and improve the accuracy of detecting stress and anxiety among female students using machine learning algorithms and functions. Three primary features are cigarette smoking, physical activity and grade point average (GPA). The multiple linear regression analysis conducted on 160 datasets obtained from the State-Trait Anxiety Inventory (STAI) at the University of Belgrade was selected. A heat map was utilised to identify the least engaging areas of the model along with most state anxiety factors. Additionally, R-squared (R2), mean absolute error (MAE), mean squared error (MSE) and root mean squared error (RMSE) were employed to assess the errors of the linear regression model for both pre-intervention and post-intervention, focusing on key features related to female students' anxiety. Using the K-Means algorithm, cluster analysis was executed on samples (N = 160) with three key features. The total average anxiety score was 44.39% (out of 80%) and is considered moderate. The heat map indicated a strong relationship between the variables. Overall, the post-intervention stage yielded acceptable results compared to the pre-intervention stage. Two clusters of anxiety among female students were identified, demonstrating that these features can accurately detect anxiety in female students. This research aims to analyse female students' stress and anxiety better using the linear regression algorithm. Additionally, ML functions demonstrated that smoking cigarettes, physical activity and GPA related to the stress and anxiety of female students have reduced errors during anxiety detection.

Establishment and validation of a nomogram for dropout intention in Chinese early year medical undergraduates

BACKGROUND

The attrition rate of Chinese medical students is high. This study utilizes a nomogram technique to develop a predictive model for dropout intention among Chinese medical undergraduates based on 19 individual and work-related characteristics.

METHOD

A repeated cross-sectional study was conducted, enrolling 3536 medical undergraduates in T1 (August 2020-April 2021) and 969 participants in T2 (October 2022) through snowball sampling. Demographics (age, sex, study phase, income, relationship status, history of mental illness) and mental health factors (including depression, anxiety, stress, burnout, alcohol use disorder, sleepiness, quality of life, fatigue, history of suicidal attempts (SA), and somatic symptoms), as well as work-related variables (career choice regret and reasons, workplace violence experience, and overall satisfaction with the Chinese healthcare environment), were gathered via questionnaires. Data from T1 was split into a training cohort and an internal validation cohort, while T2 data served as an external validation cohort. The nomogram's performance was evaluated for discrimination, calibration, clinical applicability, and generalization using receiver operating characteristic curves (ROC), area under the curve (AUC), calibration curves, and decision curve analysis (DCA).

RESULT

From 19 individual and work-related factors, five were identified as significant predictors for the construction of the nomogram: history of SA, career choice regret, experience of workplace violence, depressive symptoms, and burnout. The AUC values for the training, internal validation, and external validation cohorts were 0.762, 0.761, and 0.817, respectively. The nomogram demonstrated reliable prediction and discrimination, with adequate calibration and generalization across both the training and validation cohorts.

CONCLUSION

This nomogram exhibits reasonable accuracy in foreseeing dropout intentions among Chinese medical undergraduates. It could guide colleges, hospitals, and policymakers in pinpointing students at risk, thus informing targeted interventions. Addressing underlying factors such as depressive symptoms, burnout, career choice regret, and workplace violence may help reduce the attrition of medical undergraduates.

TRIAL REGISTRATION

This is an observational study. There is no Clinical Trial Number associated with this manuscript.

Status of mental and social activities of young and middle-aged patients after papillary thyroid cancer surgery

Background

Papillary thyroid cancer (PTC) is prevalent among younger populations and has a favorable survival rate. However, a significant number of patients experience psychosocial stress and a reduced quality of life (QoL) after surgical treatment. Therefore, comprehensive evaluations of the patients are essential to improve their recovery.

Methods

The present study enrolled 512 young and middle-aged patients diagnosed with PTC who underwent surgery at our institution between September 2020 and August 2021. Each participant completed a series of questionnaires: Generalized Anxiety Disorder 7 (GAD-7), European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30), Thyroid Cancer-Specific Quality of Life Questionnaire (THYCA-QoL), and Readiness to Return-to-Work Scale (RRTW).

Results

GAD-7 data showed that almost half of the study subjects were experiencing anxiety. Regarding health-related quality of life (HRQoL), participants reported the highest levels of fatigue, insomnia, voice problems, and scarring, with patients in anxious states reporting worse symptoms. Based on RRTW, more than half of the subjects had returned to work and had better HRQoL compared to the others who were evaluating a possible return to work. Age, gender, BMI, education, diet, residence, health insurance, months since surgery, monthly income, and caregiver status were significantly correlated with return to work. Additionally, having a caregiver, higher monthly income, more time since surgery, and living in a city or village were positively associated with return to work.

Conclusion

Young and middle-aged patients with PTC commonly experience a range of health-related issues and disease-specific symptoms following surgery, accompanied by inferior psychological well-being, HRQoL, and work readiness. It is crucial to prioritize timely interventions targeting postoperative psychological support, HRQoL improvement, and the restoration of working ability in PTC patients.