Background: The accuracy of Logical Observation Identifiers Names and Codes (LOINC) mappings is reportedly low, and the LOINC codes used for research purposes in Korea have not been validated for accuracy or usability. Our study aimed to evaluate the discrepancies and similarities in interoperability using existing LOINC mappings in actual patient care settings. Methods: We collected data on local test codes and their corresponding LOINC mappings from seven university hospitals. Our analysis focused on laboratory tests that are frequently requested, excluding clinical microbiology and molecular tests. Codes from nationwide proficiency tests served as intermediary benchmarks for comparison. A research team, comprising clinical pathologists and terminology experts, utilized the LOINC manual to reach a consensus on determining the most suitable LOINC codes. Results: A total of 235 LOINC codes were designated as optimal codes for 162 frequent tests.Among these, 51 test items, including 34 urine tests, required multiple optimal LOINC codes, primarily due to unnoted properties such as whether the test was quantitative or qualitative, or differences in measurement units. We analyzed 962 LOINC codes linked to 162 tests across seven institutions, discovering that 792 (82.3%) of these codes were consistent. Inconsistencies were most common in the analyte component (38 inconsistencies, 33.3%), followed by the method (33 inconsistencies, 28.9%), and properties (13 inconsistencies, 11.4%). Conclusion This study reveals a significant inconsistency rate of over 15% in LOINC mappings utilized for research purposes in university hospitals, underlining the necessity for expert verification to enhance interoperability in real patient care.

Objective: This study hypothesized that physical status, temperament and characteristics, and neurocognitive functions of basketball players could predict the result of Korean Basketball League (KBL) draft selection. Methods: We recruited the number of 89 college elite basketball players (KBL selection, n=44; non-KBL selection, n=45), and the number of 82 age-matched healthy comparison subjects who major in sports education in college. All participants were assessed with the Temperament and Character Inventory, Sports Anxiety Scales, Beck Depression Inventory, Perceived Stress Scale-10, Trail Making Test, and Computerized Neuro-cognitive Test for Emotional Perception and Mental Rotation. Results: Current results showed that physical status, temperament and characteristics, and neurocognitive functions of college basketball players could predict the KBL draft selection. Among temperament and characteristics, novelty seeking and reward dependence were associated with KBL draft selection. The basketball performances including average scores and average rebound were associated with Emotional Perception and Mental Rotation. Conclusion In order to be a good basketball player for a long time, it was confirmed that temperamental factors and neurocognitive factors were very closely related. Furthermore, it is also judged that these results can be used as basic data to predict potential professional basketball players.

Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

Background: Epidermal growth factor receptor (EGFR) gene mutation testing is crucial for the administration of tyrosine kinase inhibitors to treat non–small cell lung cancer. In addition to traditional tissue-based tests, liquid biopsies using plasma are increasingly utilized, particularly for detecting T790M mutations. This study compared tissue- and plasma-based EGFR testing methods. Methods: A total of 248 patients were tested for EGFR mutations using tissue and plasma samples from 2018 to 2023 at Pusan National University Yangsan Hospital. Tissue tests were performed using PANAmutyper, and plasma tests were performed using the Cobas EGFR Mutation Test v2. Results: All 248 patients underwent tissue-based EGFR testing, and 245 (98.8%) showed positive results. Of the 408 plasma tests, 237 (58.1%) were positive. For the T790M mutation, tissue biopsies were performed 87 times in 69 patients, and 30 positive cases (38.6%) were detected. Plasma testing for the T790M mutation was conducted 333 times in 207 patients, yielding 62 positive results (18.6%). Of these, 57 (27.5%) were confirmed to have the mutation via plasma testing. Combined tissue and plasma tests for the T790M mutation were positive in nine patients (13.4%), while 17 (25.4%) were positive in tissue only and 12 (17.9%) in plasma only. This mutation was not detected in 28 patients (43.3%). Conclusions Although the tissue- and plasma-based tests showed a sensitivity of 37.3% and 32.8%, respectively, combined testing increased the detection rate to 56.7%. Thus, neither test demonstrated superiority, rather, they were complementary.

Pneumocystis jirovecii pneumonia (PJP) is a life-threatening infection commonly observed in immunocompromised patients, necessitating prompt diagnosis and treatment. This review evaluates the diagnostic performance of various tests used for PJP diagnosis through a comprehensive literature review. Additionally, we propose a diagnostic algorithm tailored to non-human immunodeficiency virus immunocompromised patients, considering the specific characteristics of current medical resources in Korea.

Objective: This study hypothesized that physical status, temperament and characteristics, and neurocognitive functions of basketball players could predict the result of Korean Basketball League (KBL) draft selection. Methods: We recruited the number of 89 college elite basketball players (KBL selection, n=44; non-KBL selection, n=45), and the number of 82 age-matched healthy comparison subjects who major in sports education in college. All participants were assessed with the Temperament and Character Inventory, Sports Anxiety Scales, Beck Depression Inventory, Perceived Stress Scale-10, Trail Making Test, and Computerized Neuro-cognitive Test for Emotional Perception and Mental Rotation. Results: Current results showed that physical status, temperament and characteristics, and neurocognitive functions of college basketball players could predict the KBL draft selection. Among temperament and characteristics, novelty seeking and reward dependence were associated with KBL draft selection. The basketball performances including average scores and average rebound were associated with Emotional Perception and Mental Rotation. Conclusion In order to be a good basketball player for a long time, it was confirmed that temperamental factors and neurocognitive factors were very closely related. Furthermore, it is also judged that these results can be used as basic data to predict potential professional basketball players.

Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

Objective: This study hypothesized that physical status, temperament and characteristics, and neurocognitive functions of basketball players could predict the result of Korean Basketball League (KBL) draft selection. Methods: We recruited the number of 89 college elite basketball players (KBL selection, n=44; non-KBL selection, n=45), and the number of 82 age-matched healthy comparison subjects who major in sports education in college. All participants were assessed with the Temperament and Character Inventory, Sports Anxiety Scales, Beck Depression Inventory, Perceived Stress Scale-10, Trail Making Test, and Computerized Neuro-cognitive Test for Emotional Perception and Mental Rotation. Results: Current results showed that physical status, temperament and characteristics, and neurocognitive functions of college basketball players could predict the KBL draft selection. Among temperament and characteristics, novelty seeking and reward dependence were associated with KBL draft selection. The basketball performances including average scores and average rebound were associated with Emotional Perception and Mental Rotation. Conclusion In order to be a good basketball player for a long time, it was confirmed that temperamental factors and neurocognitive factors were very closely related. Furthermore, it is also judged that these results can be used as basic data to predict potential professional basketball players.

Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

Background: The accuracy of Logical Observation Identifiers Names and Codes (LOINC) mappings is reportedly low, and the LOINC codes used for research purposes in Korea have not been validated for accuracy or usability. Our study aimed to evaluate the discrepancies and similarities in interoperability using existing LOINC mappings in actual patient care settings. Methods: We collected data on local test codes and their corresponding LOINC mappings from seven university hospitals. Our analysis focused on laboratory tests that are frequently requested, excluding clinical microbiology and molecular tests. Codes from nationwide proficiency tests served as intermediary benchmarks for comparison. A research team, comprising clinical pathologists and terminology experts, utilized the LOINC manual to reach a consensus on determining the most suitable LOINC codes. Results: A total of 235 LOINC codes were designated as optimal codes for 162 frequent tests.Among these, 51 test items, including 34 urine tests, required multiple optimal LOINC codes, primarily due to unnoted properties such as whether the test was quantitative or qualitative, or differences in measurement units. We analyzed 962 LOINC codes linked to 162 tests across seven institutions, discovering that 792 (82.3%) of these codes were consistent. Inconsistencies were most common in the analyte component (38 inconsistencies, 33.3%), followed by the method (33 inconsistencies, 28.9%), and properties (13 inconsistencies, 11.4%). Conclusion This study reveals a significant inconsistency rate of over 15% in LOINC mappings utilized for research purposes in university hospitals, underlining the necessity for expert verification to enhance interoperability in real patient care.