Background: Hearing level reference values based on the results of recent audiometry have not been established for the general population of South Korea. This study aimed to evaluate the mean hearing levels of each age group and to measure the annual progression of hearing loss. Methods: We used the database of the eighth and ninth Korea National Health and Nutrition Examination Survey from 2020 to 2022, and included participants with normal tympanic membranes and without occupational noise exposure. Mean hearing levels were calculated by averaging hearing levels at 0.5 kHz, 1 kHz, 2 kHz, and 4 kHz. Hearing loss was defined as a hearing level exceeding 25 dB in the better ear, and severity of hearing loss was classified as moderate, moderately severe, severe, and profound, following the World Health Organization classification. Results: Hearing levels and the proportions of patients with hearing loss increased with age. Hearing levels were 9.11 ± 6.28 dB, 13.33 ± 7.85 dB, 19.90 ± 10.30 dB, and 31.95 ± 13.37 dB in those in their 40s, 50s, 60s, and 70s, respectively, and the proportions of patients with hearing loss were 1.81%, 7.51%, 25.32%, and 63.94%, respectively. The annual increase in hearing level was 0.71 dB, and increases were greater at higher frequencies. Hearing loss was also worse in men. Conclusion The present study provides reference values for normal hearing levels by age group, and indirectly reveals the progression of age-related hearing loss. Based on the results, physicians will be able to provide appropriate advice regarding hearing levels and the need for monitoring in the elderly.

Purpose: Early identification of patients at risk for acute respiratory failure (ARF) could help clinicians devise preventive strategies. Analyzing biosignals with artificial intelligence (AI) can uncover hidden information and variability within time series. We aimed to develop and validate AI models to predict ARF within 72 h after emergency department admission, primarily using highresolution biosignals collected within 4 h of arrival. Materials and Methods: Our AI model, built on convolutional recurrent neural networks, combines biosignal feature extraction and sequence modeling. The model was developed and internally validated with data from 5284 admissions [1085 (20.5%) positive for ARF], and externally validated using data from 144 admissions [7 (4.9%) positive for ARF] from another institution. We defined ARF as the application of advanced respiratory support devices. Results: Our AI model performed well in predicting ARF, achieving area under the receiver operating characteristic curve (AUROC) of 0.840 and 0.743 in internal and external validations, respectively. It outperformed the Modified Early Warning Score (MEWS) and XGBoost models built only with clinical variables. High predictive ability for mortality was observed, with AUROC up to 0.809. A 10% increase in AI prediction scores was associated with 1.44-fold and 1.42-fold increases in ARF risk and mortality risk, respectively, even after adjusting for MEWS and demographic variables. Conclusion Our AI model demonstrates high predictive accuracy and significant associations with clinical outcomes. Our AI model has the potential to promptly aid in triage decisions. Our study shows that using AI to analyze biosignals advances disease detection and prediction.

Purpose: Early identification of patients at risk for acute respiratory failure (ARF) could help clinicians devise preventive strategies. Analyzing biosignals with artificial intelligence (AI) can uncover hidden information and variability within time series. We aimed to develop and validate AI models to predict ARF within 72 h after emergency department admission, primarily using highresolution biosignals collected within 4 h of arrival. Materials and Methods: Our AI model, built on convolutional recurrent neural networks, combines biosignal feature extraction and sequence modeling. The model was developed and internally validated with data from 5284 admissions [1085 (20.5%) positive for ARF], and externally validated using data from 144 admissions [7 (4.9%) positive for ARF] from another institution. We defined ARF as the application of advanced respiratory support devices. Results: Our AI model performed well in predicting ARF, achieving area under the receiver operating characteristic curve (AUROC) of 0.840 and 0.743 in internal and external validations, respectively. It outperformed the Modified Early Warning Score (MEWS) and XGBoost models built only with clinical variables. High predictive ability for mortality was observed, with AUROC up to 0.809. A 10% increase in AI prediction scores was associated with 1.44-fold and 1.42-fold increases in ARF risk and mortality risk, respectively, even after adjusting for MEWS and demographic variables. Conclusion Our AI model demonstrates high predictive accuracy and significant associations with clinical outcomes. Our AI model has the potential to promptly aid in triage decisions. Our study shows that using AI to analyze biosignals advances disease detection and prediction.

Purpose: Early identification of patients at risk for acute respiratory failure (ARF) could help clinicians devise preventive strategies. Analyzing biosignals with artificial intelligence (AI) can uncover hidden information and variability within time series. We aimed to develop and validate AI models to predict ARF within 72 h after emergency department admission, primarily using highresolution biosignals collected within 4 h of arrival. Materials and Methods: Our AI model, built on convolutional recurrent neural networks, combines biosignal feature extraction and sequence modeling. The model was developed and internally validated with data from 5284 admissions [1085 (20.5%) positive for ARF], and externally validated using data from 144 admissions [7 (4.9%) positive for ARF] from another institution. We defined ARF as the application of advanced respiratory support devices. Results: Our AI model performed well in predicting ARF, achieving area under the receiver operating characteristic curve (AUROC) of 0.840 and 0.743 in internal and external validations, respectively. It outperformed the Modified Early Warning Score (MEWS) and XGBoost models built only with clinical variables. High predictive ability for mortality was observed, with AUROC up to 0.809. A 10% increase in AI prediction scores was associated with 1.44-fold and 1.42-fold increases in ARF risk and mortality risk, respectively, even after adjusting for MEWS and demographic variables. Conclusion Our AI model demonstrates high predictive accuracy and significant associations with clinical outcomes. Our AI model has the potential to promptly aid in triage decisions. Our study shows that using AI to analyze biosignals advances disease detection and prediction.

Background: Hearing level reference values based on the results of recent audiometry have not been established for the general population of South Korea. This study aimed to evaluate the mean hearing levels of each age group and to measure the annual progression of hearing loss. Methods: We used the database of the eighth and ninth Korea National Health and Nutrition Examination Survey from 2020 to 2022, and included participants with normal tympanic membranes and without occupational noise exposure. Mean hearing levels were calculated by averaging hearing levels at 0.5 kHz, 1 kHz, 2 kHz, and 4 kHz. Hearing loss was defined as a hearing level exceeding 25 dB in the better ear, and severity of hearing loss was classified as moderate, moderately severe, severe, and profound, following the World Health Organization classification. Results: Hearing levels and the proportions of patients with hearing loss increased with age. Hearing levels were 9.11 ± 6.28 dB, 13.33 ± 7.85 dB, 19.90 ± 10.30 dB, and 31.95 ± 13.37 dB in those in their 40s, 50s, 60s, and 70s, respectively, and the proportions of patients with hearing loss were 1.81%, 7.51%, 25.32%, and 63.94%, respectively. The annual increase in hearing level was 0.71 dB, and increases were greater at higher frequencies. Hearing loss was also worse in men. Conclusion The present study provides reference values for normal hearing levels by age group, and indirectly reveals the progression of age-related hearing loss. Based on the results, physicians will be able to provide appropriate advice regarding hearing levels and the need for monitoring in the elderly.

Purpose: Since 1995, the Korean Gastric Cancer Association (KGCA) has been periodically conducting nationwide surveys on patients with surgically treated gastric cancer. This study details the results of the survey conducted in 2023. Materials and Methods: The survey was conducted from March to December 2024 using a standardized case report form. Data were collected on 86 items, including patient demographics, tumor characteristics, surgical procedures, and surgical outcomes. The results of the 2023 survey were compared with those of previous surveys. Results: Data from 12,751 cases were collected from 66 institutions. The mean patient age was 64.6 years, and the proportion of patients aged ≥71 years increased from 9.1% in 1995 to 31.7% in 2023. The proportion of upper-third tumors slightly decreased to 16.8% compared to 20.9% in 2019. Early gastric cancer accounted for 63.1% of cases in 2023.Regarding operative procedures, a totally laparoscopic approach was most frequently applied (63.2%) in 2023, while robotic gastrectomy steadily increased to 9.5% from 2.1% in 2014.The most common anastomotic method was the Billroth II procedure (48.8%) after distal gastrectomy and double-tract reconstruction (51.9%) after proximal gastrectomy in 2023.However, the proportion of esophago-gastrostomy with anti-reflux procedures increased to 30.9%. The rates of post-operative mortality and overall complications were 1.0% and 15.3%, respectively. Conclusions The results of the 2023 nationwide survey demonstrate the current status of gastric cancer treatment in Korea. This information will provide a basis for future gastric cancer research.

Purpose: Since 1995, the Korean Gastric Cancer Association (KGCA) has been periodically conducting nationwide surveys on patients with surgically treated gastric cancer. This study details the results of the survey conducted in 2023. Materials and Methods: The survey was conducted from March to December 2024 using a standardized case report form. Data were collected on 86 items, including patient demographics, tumor characteristics, surgical procedures, and surgical outcomes. The results of the 2023 survey were compared with those of previous surveys. Results: Data from 12,751 cases were collected from 66 institutions. The mean patient age was 64.6 years, and the proportion of patients aged ≥71 years increased from 9.1% in 1995 to 31.7% in 2023. The proportion of upper-third tumors slightly decreased to 16.8% compared to 20.9% in 2019. Early gastric cancer accounted for 63.1% of cases in 2023.Regarding operative procedures, a totally laparoscopic approach was most frequently applied (63.2%) in 2023, while robotic gastrectomy steadily increased to 9.5% from 2.1% in 2014.The most common anastomotic method was the Billroth II procedure (48.8%) after distal gastrectomy and double-tract reconstruction (51.9%) after proximal gastrectomy in 2023.However, the proportion of esophago-gastrostomy with anti-reflux procedures increased to 30.9%. The rates of post-operative mortality and overall complications were 1.0% and 15.3%, respectively. Conclusions The results of the 2023 nationwide survey demonstrate the current status of gastric cancer treatment in Korea. This information will provide a basis for future gastric cancer research.

Background: Hearing level reference values based on the results of recent audiometry have not been established for the general population of South Korea. This study aimed to evaluate the mean hearing levels of each age group and to measure the annual progression of hearing loss. Methods: We used the database of the eighth and ninth Korea National Health and Nutrition Examination Survey from 2020 to 2022, and included participants with normal tympanic membranes and without occupational noise exposure. Mean hearing levels were calculated by averaging hearing levels at 0.5 kHz, 1 kHz, 2 kHz, and 4 kHz. Hearing loss was defined as a hearing level exceeding 25 dB in the better ear, and severity of hearing loss was classified as moderate, moderately severe, severe, and profound, following the World Health Organization classification. Results: Hearing levels and the proportions of patients with hearing loss increased with age. Hearing levels were 9.11 ± 6.28 dB, 13.33 ± 7.85 dB, 19.90 ± 10.30 dB, and 31.95 ± 13.37 dB in those in their 40s, 50s, 60s, and 70s, respectively, and the proportions of patients with hearing loss were 1.81%, 7.51%, 25.32%, and 63.94%, respectively. The annual increase in hearing level was 0.71 dB, and increases were greater at higher frequencies. Hearing loss was also worse in men. Conclusion The present study provides reference values for normal hearing levels by age group, and indirectly reveals the progression of age-related hearing loss. Based on the results, physicians will be able to provide appropriate advice regarding hearing levels and the need for monitoring in the elderly.