RESUMEN
Objective: The study aimed to explore the predictors of work withdrawal behavior among young lung cancer survivors and examine the mediating role of self-efficacy in the relationship between social support and work withdrawal behavior. Methods: This cross-sectional study was conducted in a cancer center in southern China. A total of 215 young lung cancer survivors were recruited from January 2021 to July 2021 and investigated by a demographic and disease-related questionnaire, the Work Withdrawal Behavior Scale, Social Support Rating Scale, and General Self-efficacy Scale. Data analysis was performed âusing âIBM SPSS 25.0 (IBM Corp., Armonk, NY, USA) and PROCESS macro version 3.3 for SPSS developed by Preacher and Hayes. Results: The mean score of work withdrawal behavior was 3.02 (±0.70). Existing symptoms, income, residence, the duration of postoperative rest time, social support, and self-efficacy were the predictors and explained 70.2% of the variance of work withdrawal behavior. The mediating effect of self-efficacy was identified between social support and work withdrawal behavior (indirect effect â= â0.36, bias-corrected 95% confidence interval [-0.542 to -0.197]). Conclusions: Work withdrawal behavior was prevalent among young lung cancer survivors. Social support and self-efficacy were significantly associated with work withdrawal behavior, and self-efficacy was identified as a mediator between social support and work withdrawal behavior among this group. Health professionals could help them return and adapt to work by relieving their symptoms, providing social support, and enhancing their self-efficacy.
RESUMEN
Background: We previously identified a 12-microRNA (miRNA) panel (miRNA-17, miRNA-146a, miRNA-200b, miRNA-182, miRNA-155, miRNA-221, miRNA-205, miRNA-126, miRNA-7, miRNA-21, miRNA-145, and miRNA-210) that aided in the early diagnosis of non-small cell lung cancer (NSCLC). We validated the diagnostic value of this miRNA panel and compared it with that of traditional tumor markers and radiological diagnosis. We constructed a nomogram based on the miRNA panel's results to predict the risk of NSCLC. Methods: Eighty-two participants with pulmonary nodules on a CT scan and who underwent a pathological examination and surgical treatment were enrolled in our study. Patients were randomly divided into a training group or a validation group. The miRNA concentrations were quantified by RT-PCR and log-transformed for analysis. The cutoff value was determined in the training group and then applied in the validation group. A comparison between the miRNAs and traditional tumor markers [CEA, NSE, and cytokeratin 19 fragment 21-1 (Cyfra21-1)] and radiological diagnosis was performed. A nomogram based on the miRNA panel's results to predict the risk of NSCLC was constructed. Results: The expression level of these 12 miRNAs was significantly higher in NSCLC patients than in benign patients. In the validation group, the specificity and positive predictive value were 96.4 and 95.8%, respectively, which were significantly higher than those using traditional tumor markers or radiological diagnosis. The sensitivity was 42.6%, which was also higher than that using tumor markers. Moreover, the sensitivity increased to 63.6% when the nodule diameters were larger than 2 cm. The miRNAs and seven clinical factors were integrated into the nomogram, and the calibration curves showed optimal agreement between the predicted and actual probabilities. Conclusions: Our miRNA panel has clinical value for the early detection of NSCLC. A nomogram was constructed and internally validated, and the results indicate that it can assist clinicians in making treatment recommendations in the clinic.