Background Staff in tertiary hospitals are a high-risk group for occupational burnout. Timely identification and precise intervention are crucial for improving healthcare service quality. However, comparative studies on perceived stress and occupational burnout among hospital staff in different positions are lacking. Objective To describe the status of perceived stress and occupational burnout among hospital staff in different positions and compare the differences, explore the relationship between perceived stress and occupational burnout, and identify the influencing factors of occupational burnout. Methods In May 2022, 1526 hospital staff members were selected by convenience sampling from six tertiary hospitals in Guangzhou. Data were collected using a general information questionnaire, the Chinese Perceived Stress Scale (CPSS), and the Maslach Burnout Inventory-Human Services Survey (MBI-HSS) to assess demographic characteristics, perceived stress, and occupational burnout. Chi-square test, one-way ANOVA, and Kruskal-Wallis H test were used to compare differences in perceived stress and occupational burnout among staff in different positions. Pearson correlation analysis was used to explore possible correlation between perceived stress and occupational burnout. Multiple linear regression was conducted to identify influencing factors of occupational burnout. Results A total of 1388 valid questionnaires were collected, with an effective response rate of 90.96%. Among them, 437 nurses (57.0%), 273 physicians (58.6%), 24 pharmacists (57.1%), and 62 administrators (54.9%) reported positive perceived health risk stress (P=0.892). Additionally, 392 nurses (51.1%), 213 physicians (45.7%), 19 pharmacists (45.2%), and 53 administrators (46.9%) reported positive occupational burnout (P=0.005). Perceived stress was positively correlated with occupational burnout among nurses, physicians, pharmacists, and administrators (r=0.5973, 0.5687, 0.5464, and 0.5276, respectively; P＜0.01). The multiple linear regression identified perceived stress (b=1.4587, P＜0.001), job satisfaction (b=−7.4195, P＜0.001), self-rated health (b=−2.0428, P＜0.001), working years (b=−0.1135, P=0.016), and being a nurse (compared with an administrators) (b=3.2435, P=0.045) as significant factors for occupational burnout (P<0.001). Conclusion Perceived health risk stress and occupational burnout are common among hospital staff, with nurses experiencing significantly higher levels of occupational burnout compared to other positions. Hospital managers should pay more attention to staff with higher perceived stress, lower job satisfaction, poorer self-rated health, and shorter working years, particularly nurses.

Objective: This study aimed to elucidate the hand function recovery capacity of degenerative cervical myelopathy (DCM) patients with different severities of hand dexterity impairment. Methods: Hand functional outcome measures such as the 10-second grip and release (10s-G&R) test, modified Japanese Orthopaedic Association (mJOA) upper extremity score and Japanese Orthopaedic Association Cervical Myelopathy Evaluation Questionnaire (JOACMEQ) upper extremity function were collected before surgery and at the 1-year follow-up. A total of 102 DCM patients were categorized into mild, moderate and severe group based on the preoperative 10s-G&R test result. Hand functional parameters were compared across the 3 groups. Multivariate linear regression was conducted to explore predictive factors. Receiver operating characteristic curve analysis was performed to assess the predictive efficacy of the preoperative 10s-G&R test and establish the cutoff value for incomplete recovery of hand dexterity. Results: At the 1-year follow-up, significant improvements were observed in all hand functional parameters across all 3 groups. However, the incomplete recovery rates of the mild, moderate, severe groups were 26.67%, 46.88%, and 57.50%, respectively (p < 0.05). Multivariate regression revealed that preoperative 10s-G&R test result, age, Hoffmann sign, duration of symptom, and mJOA Upper score serve as significant predictors for postoperative 10s-G&R test outcomes. Patients with a preoperative 10s-G&R test < 15 cycles have a 1.9 times higher risk of incomplete recovery of hand function (p = 0.005). Conclusion Most patients, regardless of their preoperative hand function, exhibit potential for improvement in hand dexterity. However, worse initial hand dexterity correlates with poorer outcomes. Surgical treatment is recommended before the 10s-G&R test drops below 15 cycles.

Background: Active surveillance (AS) has emerged as a viable management strategy for low-risk papillary thyroid microcarcinoma (PTMC), following pioneering trials at Kuma Hospital and the Cancer Institute Hospital in Japan. Numerous prospective cohort studies have since validated AS as a management option for low-risk PTMC, leading to its inclusion in thyroid cancer guidelines across various countries. From 2016 to 2020, the Multicenter Prospective Cohort Study of Active Surveillance on Papillary Thyroid Microcarcinoma (MAeSTro) enrolled 1,177 patients, providing comprehensive data on PTMC progression, sonographic predictors of progression, quality of life, surgical outcomes, and cost-effectiveness when comparing AS to immediate surgery. The second phase of MAeSTro (MAeSTro-EXP) expands AS to low-risk papillary thyroid carcinoma (PTC) tumors larger than 1 cm, driven by the hypothesis that overall risk assessment outweighs absolute tumor size in surgical decision-making. Methods: This protocol aims to address whether limiting AS to tumors smaller than 1 cm may result in unnecessary surgeries for low-risk PTCs detected during their rapid initial growth phase. By expanding the AS criteria to include tumors up to 1.5 cm, while simultaneously refining and standardizing the criteria for risk assessment and disease progression, we aim to minimize overtreatment and maintain rigorous monitoring to improve patient outcomes. Conclusion This study will contribute to optimizing AS guidelines and enhance our understanding of the natural course and appropriate management of low-risk PTCs. Additionally, MAeSTro-EXP involves a multinational collaboration between South Korea and Australia. This cross-country study aims to identify cultural and racial differences in the management of low-risk PTC, thereby enriching the global understanding of AS practices and their applicability across diverse populations.

Background/Aims: Inaccurate prediction of lymph node metastasis (LNM) may lead to unnecessary surgery following endoscopic resection of T1 colorectal cancer (CRC). We aimed to validate the usefulness of artificial intelligence (AI) models for predicting LNM in patients with T1 CRC. Methods: We analyzed the clinical data, laboratory results, pathological reports, and endoscopic findings of patients who underwent radical surgery for T1 CRC. We developed AI models to predict LNM using four algorithms: regularized logistic regression classifier (RLRC), random forest classifier (RFC), CatBoost classifier (CBC), and the voting classifier (VC). Four histological factors and four endoscopic findings were included to develop AI models. Areas under the receiver operating characteristics curves (AUROCs) were measured to distinguish AI model performance in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines. Results: Among 1,386 patients with T1 CRC, 173 patients (12.5%) had LNM. The AUROC values of the RLRC, RFC, CBC, and VC models for LNM prediction were significantly higher (0.673, 0.640, 0.679, and 0.677, respectively) than the 0.525 suggested in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines (vs RLRC, p<0.001; vs RFC, p=0.001; vs CBC, p<0.001; vs VC, p<0.001). The AUROC value was similar between T1 colon versus T1 rectal cancers (0.718 vs 0.615, p=0.700). The AUROC value was also similar between the initial endoscopic resection and initial surgery groups (0.581 vs 0.746, p=0.845). Conclusions AI models trained on the basis of endoscopic findings and pathological features performed well in predicting LNM in patients with T1 CRC regardless of tumor location and initial treatment method.

Objectives: . A substantial proportion of patients with head and neck cancer (HNC) require emergency room (ER) visits or unplanned hospitalizations during or after treatment with various modalities. We investigated HNC cases that necessitated ER visitation after cancer treatment, aiming to identify potential risk factors in the context of the Korean healthcare system. Methods: . This single-center cohort study examined patients with HNC who received cancer treatments at Samsung Medical Center in 2019 (n=566). Treatment modalities included surgery alone (n=184), surgery and adjuvant therapy (n=138), curative non-surgical treatment such as radiation or chemoradiation (n=209), and palliative treatments (n=35). We followed these cases for up to 3 years, focusing on those who visited the ER during or after cancer treatment, and analyzed the primary reasons and risk factors associated with these visits. Results: . The ER visitation rate was 8.0% (n=45) among patients with HNC, with a total of 70 ER visits (12.4%; mean, 1.56; range, 1–4). The rate of treatment-related ER visitation was 4.6%. Common reasons for ER visits included surgical site or wound complications (31.1% of patients visiting the ER, 22.9% of ER visits) and issues with oral intake or feeding (22.2% of patients, 31.4% of visits). Significant risk factors for ER visits included tumor subsite (with hypopharyngeal cancer associated with a 17.9% rate of treatment-related ER visits), tumor stage (T2–4, 8.6%–12.2%; N+ status, 6.7%), and treatment modality (surgery with adjuvant chemoradiation, 19.4%). Patient age and comorbidities did not represent significant factors. Conclusion . The most frequent reasons for ER visits among patients with HNC included complications with wounds and feeding. Additionally, tumor characteristics and treatment modality were independent risk factors for ER visits. Adequate planning and management to address these issues could potentially decrease the number of ER visits, lower costs, and improve patient care.

Background: Active surveillance (AS) has emerged as a viable management strategy for low-risk papillary thyroid microcarcinoma (PTMC), following pioneering trials at Kuma Hospital and the Cancer Institute Hospital in Japan. Numerous prospective cohort studies have since validated AS as a management option for low-risk PTMC, leading to its inclusion in thyroid cancer guidelines across various countries. From 2016 to 2020, the Multicenter Prospective Cohort Study of Active Surveillance on Papillary Thyroid Microcarcinoma (MAeSTro) enrolled 1,177 patients, providing comprehensive data on PTMC progression, sonographic predictors of progression, quality of life, surgical outcomes, and cost-effectiveness when comparing AS to immediate surgery. The second phase of MAeSTro (MAeSTro-EXP) expands AS to low-risk papillary thyroid carcinoma (PTC) tumors larger than 1 cm, driven by the hypothesis that overall risk assessment outweighs absolute tumor size in surgical decision-making. Methods: This protocol aims to address whether limiting AS to tumors smaller than 1 cm may result in unnecessary surgeries for low-risk PTCs detected during their rapid initial growth phase. By expanding the AS criteria to include tumors up to 1.5 cm, while simultaneously refining and standardizing the criteria for risk assessment and disease progression, we aim to minimize overtreatment and maintain rigorous monitoring to improve patient outcomes. Conclusion This study will contribute to optimizing AS guidelines and enhance our understanding of the natural course and appropriate management of low-risk PTCs. Additionally, MAeSTro-EXP involves a multinational collaboration between South Korea and Australia. This cross-country study aims to identify cultural and racial differences in the management of low-risk PTC, thereby enriching the global understanding of AS practices and their applicability across diverse populations.

Background/Aims: Inaccurate prediction of lymph node metastasis (LNM) may lead to unnecessary surgery following endoscopic resection of T1 colorectal cancer (CRC). We aimed to validate the usefulness of artificial intelligence (AI) models for predicting LNM in patients with T1 CRC. Methods: We analyzed the clinical data, laboratory results, pathological reports, and endoscopic findings of patients who underwent radical surgery for T1 CRC. We developed AI models to predict LNM using four algorithms: regularized logistic regression classifier (RLRC), random forest classifier (RFC), CatBoost classifier (CBC), and the voting classifier (VC). Four histological factors and four endoscopic findings were included to develop AI models. Areas under the receiver operating characteristics curves (AUROCs) were measured to distinguish AI model performance in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines. Results: Among 1,386 patients with T1 CRC, 173 patients (12.5%) had LNM. The AUROC values of the RLRC, RFC, CBC, and VC models for LNM prediction were significantly higher (0.673, 0.640, 0.679, and 0.677, respectively) than the 0.525 suggested in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines (vs RLRC, p<0.001; vs RFC, p=0.001; vs CBC, p<0.001; vs VC, p<0.001). The AUROC value was similar between T1 colon versus T1 rectal cancers (0.718 vs 0.615, p=0.700). The AUROC value was also similar between the initial endoscopic resection and initial surgery groups (0.581 vs 0.746, p=0.845). Conclusions AI models trained on the basis of endoscopic findings and pathological features performed well in predicting LNM in patients with T1 CRC regardless of tumor location and initial treatment method.

Background: Active surveillance (AS) has emerged as a viable management strategy for low-risk papillary thyroid microcarcinoma (PTMC), following pioneering trials at Kuma Hospital and the Cancer Institute Hospital in Japan. Numerous prospective cohort studies have since validated AS as a management option for low-risk PTMC, leading to its inclusion in thyroid cancer guidelines across various countries. From 2016 to 2020, the Multicenter Prospective Cohort Study of Active Surveillance on Papillary Thyroid Microcarcinoma (MAeSTro) enrolled 1,177 patients, providing comprehensive data on PTMC progression, sonographic predictors of progression, quality of life, surgical outcomes, and cost-effectiveness when comparing AS to immediate surgery. The second phase of MAeSTro (MAeSTro-EXP) expands AS to low-risk papillary thyroid carcinoma (PTC) tumors larger than 1 cm, driven by the hypothesis that overall risk assessment outweighs absolute tumor size in surgical decision-making. Methods: This protocol aims to address whether limiting AS to tumors smaller than 1 cm may result in unnecessary surgeries for low-risk PTCs detected during their rapid initial growth phase. By expanding the AS criteria to include tumors up to 1.5 cm, while simultaneously refining and standardizing the criteria for risk assessment and disease progression, we aim to minimize overtreatment and maintain rigorous monitoring to improve patient outcomes. Conclusion This study will contribute to optimizing AS guidelines and enhance our understanding of the natural course and appropriate management of low-risk PTCs. Additionally, MAeSTro-EXP involves a multinational collaboration between South Korea and Australia. This cross-country study aims to identify cultural and racial differences in the management of low-risk PTC, thereby enriching the global understanding of AS practices and their applicability across diverse populations.

Background/Aims: Inaccurate prediction of lymph node metastasis (LNM) may lead to unnecessary surgery following endoscopic resection of T1 colorectal cancer (CRC). We aimed to validate the usefulness of artificial intelligence (AI) models for predicting LNM in patients with T1 CRC. Methods: We analyzed the clinical data, laboratory results, pathological reports, and endoscopic findings of patients who underwent radical surgery for T1 CRC. We developed AI models to predict LNM using four algorithms: regularized logistic regression classifier (RLRC), random forest classifier (RFC), CatBoost classifier (CBC), and the voting classifier (VC). Four histological factors and four endoscopic findings were included to develop AI models. Areas under the receiver operating characteristics curves (AUROCs) were measured to distinguish AI model performance in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines. Results: Among 1,386 patients with T1 CRC, 173 patients (12.5%) had LNM. The AUROC values of the RLRC, RFC, CBC, and VC models for LNM prediction were significantly higher (0.673, 0.640, 0.679, and 0.677, respectively) than the 0.525 suggested in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines (vs RLRC, p<0.001; vs RFC, p=0.001; vs CBC, p<0.001; vs VC, p<0.001). The AUROC value was similar between T1 colon versus T1 rectal cancers (0.718 vs 0.615, p=0.700). The AUROC value was also similar between the initial endoscopic resection and initial surgery groups (0.581 vs 0.746, p=0.845). Conclusions AI models trained on the basis of endoscopic findings and pathological features performed well in predicting LNM in patients with T1 CRC regardless of tumor location and initial treatment method.

Objectives: . A substantial proportion of patients with head and neck cancer (HNC) require emergency room (ER) visits or unplanned hospitalizations during or after treatment with various modalities. We investigated HNC cases that necessitated ER visitation after cancer treatment, aiming to identify potential risk factors in the context of the Korean healthcare system. Methods: . This single-center cohort study examined patients with HNC who received cancer treatments at Samsung Medical Center in 2019 (n=566). Treatment modalities included surgery alone (n=184), surgery and adjuvant therapy (n=138), curative non-surgical treatment such as radiation or chemoradiation (n=209), and palliative treatments (n=35). We followed these cases for up to 3 years, focusing on those who visited the ER during or after cancer treatment, and analyzed the primary reasons and risk factors associated with these visits. Results: . The ER visitation rate was 8.0% (n=45) among patients with HNC, with a total of 70 ER visits (12.4%; mean, 1.56; range, 1–4). The rate of treatment-related ER visitation was 4.6%. Common reasons for ER visits included surgical site or wound complications (31.1% of patients visiting the ER, 22.9% of ER visits) and issues with oral intake or feeding (22.2% of patients, 31.4% of visits). Significant risk factors for ER visits included tumor subsite (with hypopharyngeal cancer associated with a 17.9% rate of treatment-related ER visits), tumor stage (T2–4, 8.6%–12.2%; N+ status, 6.7%), and treatment modality (surgery with adjuvant chemoradiation, 19.4%). Patient age and comorbidities did not represent significant factors. Conclusion . The most frequent reasons for ER visits among patients with HNC included complications with wounds and feeding. Additionally, tumor characteristics and treatment modality were independent risk factors for ER visits. Adequate planning and management to address these issues could potentially decrease the number of ER visits, lower costs, and improve patient care.