Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Background/Aims: We investigated the clinical practice patterns of Korean endoscopists for the endoscopic resection of colorectal polyps. Methods: From September to November 2021, an online survey was conducted regarding the preferred resection methods for colorectal polyps, and responses were compared with the international guidelines. Results: Among 246 respondents, those with <4 years, 4–9 years, and ≥10 years of experiencein colonoscopy practices accounted for 25.6%, 34.1%, and 40.2% of endoscopists, respectively. The most preferred resection methods for non-pedunculated lesions were cold forceps polypectomy for ≤3 mm lesions (81.7%), cold snare polypectomy for 4–5 mm (61.0%) and 6–9 mm (43.5%) lesions, hot endoscopic mucosal resection (EMR) for 10–19 mm lesions (72.0%), precut EMR for 20–25 mm lesions (22.0%), and endoscopic submucosal dissection (ESD) for ≥26 mm lesions (29.3%). Hot EMR was favored for pedunculated lesions with a head size <20 mm and stalk size <10 mm (75.6%) and for those with a head size ≥20 mm or stalk size ≥10 mm (58.5%). For suspected superficial and deep submucosal lesions measuring 10–19 mm and ≥20 mm, ESD (26.0% and 38.6%) and surgery (36.6% and 46.3%) were preferred, respectively. The adherence rate to the guidelines ranged from 11.2% to 96.9%, depending on the size, shape, and histology of the lesions. Conclusions Adherence to the guidelines for endoscopic resection techniques varied depend-ing on the characteristics of colorectal polyps. Thus, an individualized approach is required to increase adherence to the guidelines.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Background/Aims: We investigated the clinical practice patterns of Korean endoscopists for the endoscopic resection of colorectal polyps. Methods: From September to November 2021, an online survey was conducted regarding the preferred resection methods for colorectal polyps, and responses were compared with the international guidelines. Results: Among 246 respondents, those with <4 years, 4–9 years, and ≥10 years of experiencein colonoscopy practices accounted for 25.6%, 34.1%, and 40.2% of endoscopists, respectively. The most preferred resection methods for non-pedunculated lesions were cold forceps polypectomy for ≤3 mm lesions (81.7%), cold snare polypectomy for 4–5 mm (61.0%) and 6–9 mm (43.5%) lesions, hot endoscopic mucosal resection (EMR) for 10–19 mm lesions (72.0%), precut EMR for 20–25 mm lesions (22.0%), and endoscopic submucosal dissection (ESD) for ≥26 mm lesions (29.3%). Hot EMR was favored for pedunculated lesions with a head size <20 mm and stalk size <10 mm (75.6%) and for those with a head size ≥20 mm or stalk size ≥10 mm (58.5%). For suspected superficial and deep submucosal lesions measuring 10–19 mm and ≥20 mm, ESD (26.0% and 38.6%) and surgery (36.6% and 46.3%) were preferred, respectively. The adherence rate to the guidelines ranged from 11.2% to 96.9%, depending on the size, shape, and histology of the lesions. Conclusions Adherence to the guidelines for endoscopic resection techniques varied depend-ing on the characteristics of colorectal polyps. Thus, an individualized approach is required to increase adherence to the guidelines.

Background/Aims: We investigated the clinical practice patterns of Korean endoscopists for the endoscopic resection of colorectal polyps. Methods: From September to November 2021, an online survey was conducted regarding the preferred resection methods for colorectal polyps, and responses were compared with the international guidelines. Results: Among 246 respondents, those with <4 years, 4–9 years, and ≥10 years of experiencein colonoscopy practices accounted for 25.6%, 34.1%, and 40.2% of endoscopists, respectively. The most preferred resection methods for non-pedunculated lesions were cold forceps polypectomy for ≤3 mm lesions (81.7%), cold snare polypectomy for 4–5 mm (61.0%) and 6–9 mm (43.5%) lesions, hot endoscopic mucosal resection (EMR) for 10–19 mm lesions (72.0%), precut EMR for 20–25 mm lesions (22.0%), and endoscopic submucosal dissection (ESD) for ≥26 mm lesions (29.3%). Hot EMR was favored for pedunculated lesions with a head size <20 mm and stalk size <10 mm (75.6%) and for those with a head size ≥20 mm or stalk size ≥10 mm (58.5%). For suspected superficial and deep submucosal lesions measuring 10–19 mm and ≥20 mm, ESD (26.0% and 38.6%) and surgery (36.6% and 46.3%) were preferred, respectively. The adherence rate to the guidelines ranged from 11.2% to 96.9%, depending on the size, shape, and histology of the lesions. Conclusions Adherence to the guidelines for endoscopic resection techniques varied depend-ing on the characteristics of colorectal polyps. Thus, an individualized approach is required to increase adherence to the guidelines.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Background/Aims: We investigated the clinical practice patterns of Korean endoscopists for the endoscopic resection of colorectal polyps. Methods: From September to November 2021, an online survey was conducted regarding the preferred resection methods for colorectal polyps, and responses were compared with the international guidelines. Results: Among 246 respondents, those with <4 years, 4–9 years, and ≥10 years of experiencein colonoscopy practices accounted for 25.6%, 34.1%, and 40.2% of endoscopists, respectively. The most preferred resection methods for non-pedunculated lesions were cold forceps polypectomy for ≤3 mm lesions (81.7%), cold snare polypectomy for 4–5 mm (61.0%) and 6–9 mm (43.5%) lesions, hot endoscopic mucosal resection (EMR) for 10–19 mm lesions (72.0%), precut EMR for 20–25 mm lesions (22.0%), and endoscopic submucosal dissection (ESD) for ≥26 mm lesions (29.3%). Hot EMR was favored for pedunculated lesions with a head size <20 mm and stalk size <10 mm (75.6%) and for those with a head size ≥20 mm or stalk size ≥10 mm (58.5%). For suspected superficial and deep submucosal lesions measuring 10–19 mm and ≥20 mm, ESD (26.0% and 38.6%) and surgery (36.6% and 46.3%) were preferred, respectively. The adherence rate to the guidelines ranged from 11.2% to 96.9%, depending on the size, shape, and histology of the lesions. Conclusions Adherence to the guidelines for endoscopic resection techniques varied depend-ing on the characteristics of colorectal polyps. Thus, an individualized approach is required to increase adherence to the guidelines.