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)

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)

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)

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)

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: s/Aims: Reported incidence of extrahepatic bile duct cancer is higher in Asians than in Western populations. Korea, in particular, is one of the countries with the highest incidence rates of extrahepatic bile duct cancer in the world. Although research and innovative therapeutic modalities for extrahepatic bile duct cancer are emerging, clinical guidelines are currently unavailable in Korea. The Korean Society of Hepato-Biliary-Pancreatic Surgery in collaboration with related societies (Korean Pancreatic and Biliary Surgery Society, Korean Society of Abdominal Radiology, Korean Society of Medical Oncology, Korean Society of Radiation Oncology, Korean Society of Pathologists, and Korean Society of Nuclear Medicine) decided to establish clinical guideline for extrahepatic bile duct cancer in June 2021. Methods: Contents of the guidelines were developed through subgroup meetings for each key question and a preliminary draft was finalized through a Clinical Guidelines Committee workshop. Results: In November 2021, the finalized draft was presented for public scrutiny during a formal hearing. Conclusions The extrahepatic guideline committee believed that this guideline could be helpful in the treatment of patients.

The aim of this study was to develop evidence-based recommendations for determining the surgical extent in patients with locally invasive differentiated thyroid cancer (DTC). Locally invasive DTC with gross extrathyroidal extension invading surrounding anatomical structures may lead to several functional deficits and poor oncological outcomes. At present, the optimal extent of surgery in locally invasive DTC remains a matter of debate, and there are no adequate guidelines. On October 8, 2021, four experts searched the PubMed, Embase, and Cochrane Library databases; the identified papers were reviewed by 39 experts in thyroid and head and neck surgery. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach was used to assess the quality of evidence, and to develop and report recommendations. The strength of a recommendation reflects the confidence of a guideline panel that the desirable effects of an intervention outweigh any undesirable effects, across all patients for whom the recommendation is applicable. After completing the draft guidelines, Delphi questionnaires were completed by members of the Korean Society of Head and Neck Surgery. Twenty-seven evidence-based recommendations were made for several factors, including the preoperative workup; surgical extent of thyroidectomy; surgery for cancer invading the strap muscles, recurrent laryngeal nerve, laryngeal framework, trachea, or esophagus; and surgery for patients with central and lateral cervical lymph node involvement. Evidence-based guidelines were devised to help clinicians make safer and more efficient clinical decisions for the optimal surgical treatment of patients with locally invasive DTC.

Objectives: After the Korean Medication Algorithm Project for Bipolar Disorder (KMAP-BP) was developed in 2002, its fifth revision was completed in 2022 to reflect the recent rapid developments and research into bipolar disorder and its psychopharmacology. Methods: According to the methodology for previous versions, the depressive episode section of KMAP-BP 2022 was revised based on a survey consisting of 11 questions. Among ninetythree experts, eighty-seven members of the review committee (93.5%) completed the survey.The executive committee analyzed the results and discussed the final production of an algorithm by considering the scientific evidence. Results: Overall, the results from this study showed little change in comparison with previous versions of KMAP-BP. However, there have been significant changes in recommendations over the span of about 20 years. The preferences for lamotrigine and atypical antipsychotics, especially aripiprazole, quetiapine, and olanzapine, have shown a tendency to continuously increase, but the preferences for risperidone and ziprasidone have not increased, but have decreased. Moreover, the preference for typical antipsychotics has significantly decreased. Additionally, concerns over the use of antidepressants in bipolar depression have been raised, and their use is not recommended in KMAP-BP 2022 as a first-line treatment. Conclusion Pharmacotherapy for acute depressive episodes with various clinical progressions and various subtypes still shows diversity, compared to pharmacotherapy for mania. We look forward to the development of bipolar depressive, episode-specific therapeutic drugs in the future, and hope the fifth update of KMAP-BP will be a complementary option for clinicians and their patients with bipolar disorder.

Purpose: This study examined the patient-reported outcome scores at mid-term follow-up after arthroscopic labral preservation in patients with borderline hip dysplasia. Materials and Methods: Data were collected retrospectively from patients who underwent arthroscopic labral preservation. Among the 61 patients classified with hip dysplasia, patients with arthritis beyond Tönnis grade II were excluded, Patients with subchondral bony exposure were excluded even if they had arthritis of Tönnis grade I. Forty-two patients with borderline hip dysplasia were enrolled among patients whose lateral center edge angle (LCEA) was between 18° and 25°. The PRO scores including the modified Harris hip score (mHHS), hip outcome score activities of daily living (HOS-ADL), hip outcome score sport-specific subscale (HOS-SSS) and visual analog scale (VAS), were collected and compared preoperatively, and then at one year and a minimum of five years after surgery. Results: The mean preoperative LCEA and Tönnis angle were 20.9° (range, 18° to 25°) and 6.3° (range, 0° to 18°), respectively. The mean follow-up was 64.8 (range, 60 to 84) months. The mean mHHS increased from 73.5 before surgery to 86.2 in the fifth year after surgery (p<0.01), the mean HOS-ADL from 70.5% before surgery to 84.9% in the fifth year after surgery (p<0.01), and the mean HOS-SSS from 51.4 before surgery to 69.4 in the fifth year after surgery (p<0.01). The mean VAS score improved from 6.3 before surgery to 1.5 in the fifth year after surgery (p<0.01). Conclusion Arthroscopic labral preservation performed in appropriately selected patients with borderline dysplasia showed good results at mid-term follow-up. In borderline hip dysplasia without the progress of osteoarthritis and subchondral bone exposure, arthroscopic labral preservation may be considered a good and less invasive option.

Objectives: . We aimed to assess the genetic differences between cases of early-stage tongue cancer that were positive or negative for lymph node metastasis. Methods: . In total, 35 cases of tongue cancer with RNA sequencing data were enrolled in this study. The gene expression profile of the following two groups was compared: N0 group (T stage 1 or 2 with N0 stage) and N+ group (T stage 1 or 2 with N+ stage). Using the R and limma packages in the Bioconductor program, we extracted the differentially expressed genes (DEGs). Gene ontology and pathway enrichment analysis were performed using the Database for Annotation, Visualization and Integration Discovery (DAVID) online tool. Immune cell infiltration was analyzed using the CIBERSORT online program. Immunochemical staining of the cancer tissue was evaluated and The Cancer Genome Atlas (TCGA) data were analyzed to validate the identified DEGs. Results: . No significant differences were found in the infiltration of 22 types of immune cells. Among a total of 51 identified DEGs, 14 genes were significantly upregulated, while 37 genes were significantly downregulated (P<0.01; fold change >2). Pathway analysis revealed significant associations with the arachidonic acid metabolism-related pathway, calcium signaling, and the muscle contraction pathway. The following DEGs were the most significantly different between the two groups: DEFB4A, SPRR2B, DEFB103B, SPRR2G, DEFB4B, and FAM25A. TCGA data showed that DEFB4A and DEFB103B were more highly expressed in the N0 group than in the N+ group, although the difference did not achieve statistical significance. Immunochemical staining of cancer tissue revealed significantly higher expression of defensin in the N0 group. Conclusions . Defensin (DEFB4A, DEFB103B, DEFB4B) may be a novel biomarker for early regional metastasis in T1/2 tongue cancer.