Hysteroscopy is particularly valuable for the diagnosis of uterine cavity abnormalities through direct visualization. The development of office hysteroscopy has expanded the range of diagnostic and surgical procedures available. These detailed guidelines include patient counseling and the selection and setting of office hysteroscopy, including room, equipment, and medical staff. Analgesia or local anesthesia is often required in selective office hysteroscopy cases. Cervical dilation and preparation using medical or mechanical methods are required for most diagnostic hysteroscopic procedures. Methods for optimizing visualization and choosing suitable distension media are important for a successful office hysteroscopy. It is crucial to adhere to guidelines to prevent complications, such as vasovagal syncope, cervical trauma, uterine perforation, fluid overload, and embolism. Vaginoscopy can be a good alternative option for alleviating pain, especially in cases where the insertion of a vaginal speculum is expected to be challenging.

Hysteroscopy is particularly valuable for the diagnosis of uterine cavity abnormalities through direct visualization. The development of office hysteroscopy has expanded the range of diagnostic and surgical procedures available. These detailed guidelines include patient counseling and the selection and setting of office hysteroscopy, including room, equipment, and medical staff. Analgesia or local anesthesia is often required in selective office hysteroscopy cases. Cervical dilation and preparation using medical or mechanical methods are required for most diagnostic hysteroscopic procedures. Methods for optimizing visualization and choosing suitable distension media are important for a successful office hysteroscopy. It is crucial to adhere to guidelines to prevent complications, such as vasovagal syncope, cervical trauma, uterine perforation, fluid overload, and embolism. Vaginoscopy can be a good alternative option for alleviating pain, especially in cases where the insertion of a vaginal speculum is expected to be challenging.

Background: An ideal lung allocation system should reduce waiting list deaths, improve transplant survival, and ensure equitable organ allocation. This study aimed to develop a novel lung allocation score (LAS) system, the MaxBenefit LAS, to maximize transplant benefits. Methods: This study retrospectively analyzed data from the Korean Network for Organ Sharing database, including 1,599 lung transplant candidates between September 2009 and December 2020. We developed the MaxBenefit LAS, combining a waitlist mortality model and a post-transplant survival model using elastic-net Cox regression, was assessed using area under the curve (AUC) values and Uno’s C-index. Its performance was compared to the US LAS in an independent cohort. Results: The waitlist mortality model showed strong predictive performance with AUC values of 0.834 and 0.818 in the training and validation cohorts, respectively. The post-transplant survival model also demonstrated good predictive ability (AUC: 0.708 and 0.685). The MaxBenefit LAS effectively stratified patients by risk, with higher scores correlating with increased waitlist mortality and decreased post-transplant mortality. The MaxBenefit LAS outperformed the conventional LAS in predicting waitlist death and identifying candidates with higher transplant benefits. Conclusion The MaxBenefit LAS offers a promising approach to optimizing lung allocation by balancing the urgency of candidates with their likelihood of survival post-transplant. This novel system has the potential to improve outcomes for lung transplant recipients and reduce waitlist mortality, providing a more equitable allocation of donor lungs.

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.

Despite widespread coronavirus disease 2019 (COVID-19) vaccine use, research on the association between vaccines and cerebrovascular venous sinus thrombosis (CVST) in diverse populations is limited. This study aimed to address this gap. Data from the World Health Organization pharmacovigilance database (1968–2024; total reports = 8,909,484) were used.Reporting odds ratios (RORs) and information components (ICs) were calculated to assess the association between each drug and CVST. In total, 851 cases were identified as vaccineassociated CVST, of which 527 (61.93%) occurred in female patients. Only Ad5-vectored COVID-19 vaccines had the highest ROR and IC value with CVST (ROR, 4.78; 95% confidence interval, 4.34–5.28; IC, 2.15). The risk of CVST increased with age, with the 45–64-years age group having an IC of 1.35, while the 65 years and older group had a higher IC of 2.08.The findings highlight the need for clinicians to recognize the potential risks of CVST and prioritize rigorous monitoring and research to ensure patient safety.

Objectives: To evaluate changes in probing depth, bleeding on probing, and three-dimensional plaque distribution after using an interdental brush for three months. Methods: This was a split-mouth design, examiner-blinded, randomized controlled trial. Fifteen patients were randomly assigned to use an interdental brush between their maxillary left or right 1st and 2nd premolar. They were instructed not to use an interdental brush on the opposite side for three months. Probing depth, bleeding on probing, bleeding on using an interdental brush, gingival recession, and plaque distribution were assessed at baseline and after three months. Results: After using an interdental brush for three months, 4.26±15.16% of plaque on interdental surfaces decreased. Bleeding on probing and bleeding on using an interdental brush also decreased by 16.67% and 40%, respectively. The size of interdental areas increased by 0.16 mm when using an interdental brush. There were no statistically significant changes in probing depth or gingival recession. Conclusions An interdental brush is an effective interdental cleaning aid that reduces interdental plaque and decreases inflammation of interdental soft tissues.

It is difficult to determine a cause of bile duct stricture and dilatation. Eosinophilic cholangitis, a rare benign condition, may be one cause of bile duct stricture and dilatation. It can be evaluated using various methods of histopathology, radiographs, endoscopy, and hematologic findings. Treatment generally involves steroid therapy which can lead to improvement. This case report will discuss eosinophilic cholangitis, emphasizing that while it can easily be overlooked but should be considered in differential diagnoses.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Objective: Objective of this study is to evaluate the association between high risk of depression and metabolic diseases such as hypertension, diabetes, and dyslipidemia in Korean cervical cancer patients. Methods: A total of 330 women with cervical cancer were included in this study, using data from the Korea National Health and Nutrition Examination Survey from 2010 to 2021. Participants were categorized into two groups—high risk of depression and non-depression—based on their answers to survey items related to depression. A multivariate logistic regression analysis was used to evaluate the influence of metabolic diseases on high risk of depression in patients with cervical cancer. Results: A total of 78 (23.64%) and 252 (76.36%) women were classified into the high risk of depression and non-depression groups, respectively. In multivariate logistic regression analysis adjusting for age, menopausal status, and smoking status, diabetes was associated with an odds ratio of 2.47 (95% confidence interval: 1.205, 5.071) for high risk of depression in cervical cancer patients. However, among the metabolic diseases, hypertension, and dyslipidemia were not associated with high risk of depression in patients with cervical cancer. Conclusion This study suggests that diabetes may be associated with a increased risk of high risk of depression in cervical cancer patients. Therefore, appropriate treatment of diabetes in cervical cancer patients may contribute to lowering the risk of depression in the future.