Myopia, particularly high myopia, is a significant risk factor for several ocular pathologies including cataract, glaucoma, and retinal detachment. Excessive axial elongation associated with high myopia can induce biomechanical stretching, increasing the risk of serious complications like posterior staphyloma and myopic maculopathy. Global meta-analyses estimate that approximately 10 million people were visually impaired due to myopic maculopathy in 2015, with 3 million being blind. Recent nationwide surveys in South Korea revealed a prevalence of 65.4% for myopia and 6.9% for high myopia in children and adolescents, highlighting the urgent need for effective management. Delaying the onset and slowing the progression of myopia during childhood and adolescence is crucial for reducing the potential lifetime risk of these complications. This consensus statement, prepared by the Korean Myopia Society for the Korean Association for Pediatric Ophthalmology and Strabismus (KAPOS), reviews the current evidence for myopia control interventions and provides management strategies applicable to the South Korean clinical setting. Key interventions covered include lifestyle modifications (outdoor time, near work adjustment), optical methods (myopia-control spectacle lenses, dual-focus soft contact lenses, orthokeratology), and pharmacologic treatment (low-concentration atropine), as well as combination therapies. The statement also addresses patient selection, treatment outcome evaluation using spherical equivalent and axial length changes, and the crucial aspects related to treatment cessation and the rebound effect.

This study examined sleep-related characteristics among university students and the associations between sleep quality, dietary habits, and diet quality.Three hundred and ninety-three university students (206 men and 187 women) enrolled at universities in the Chungcheong region of Korea participated in this study between November and December 2023. Data on the general characteristics, dietary habits, sleep status, and the Nutrition Quotient (NQ) for adults were collected using a structured questionnaire. The sleep quality was assessed using the Pittsburgh Sleep Quality Index– Korean version (PSQI-K). The participants were classified into a good sleeper group (PSQI-K≤5) and a poor sleeper group (PSQI-K≥6). The dietary habits and NQ scores were compared according to the sleep quality. Compared with good sleepers, poor sleepers had a significantly higher prevalence of eating alone at least once per day (P<0.01) and higher scores for unhealthy dietary behaviors, including overeating, eating quickly, a preference for salty food, skipping meals, and irregular meal timing (P<0.05). Although the total NQ scores and balance and practice domain scores did not differ according to sleep quality, the moderation domain scores were significantly lower among poor sleepers, particularly in men (P<0.05). In conclusion, poor sleep quality among university students was associated with unhealthy dietary habits and lower diet quality related to dietary restraint. These findings highlight the importance of improving sleep quality as a part of health education and dietary intervention programs for university students.

Background: There are limited long-term clinical outcome data comparing first-generation (1G) versus second-generation (2G) drug-eluting stents (DES) in patients with single-vessel disease (SVD). We sought to compare the efficacy and safety of 1G- and 2G-DES in SVD patients who underwent successful percutaneous coronary intervention (PCI) over 10 years of clinical follow-up. Methods: A total of 2,312 consecutive patients with SVD who underwent PCI with 1G-DES (paclitaxel- or sirolimus-eluting stents, n = 811) or 2G-DES (zotarolimus [Endeavor, Endeavor Resolute]- or everolimus-eluting stents [Promus Element, Xience], n = 1,082) were enrolled.After propensity score matching, 2 matched groups (538 pairs) were generated. Study endpoints included individual and composite clinical outcomes through 10 years. Results: During the 10-year follow-up, the 2G-DES group had lower incidences of myocardial infarction (MI; hazard ratio [HR], 0.44; 95% confidence interval [CI], 0.22–0.92; P = 0.028) and target lesion revascularization (TLR; HR, 0.54; 95% CI, 0.32–0.91; P = 0.021) than the 1G-DES group. Rates of total death, cardiac death, non-target vessel revascularization, stroke, major adverse cardiac events, and major adverse cardiac and cerebrovascular events were similar between groups. Conclusions In our single-center, all-comer registry, 2G-DES were associated with lower incidence rates of MI and TLR compared to 1G-DES in SVD patients over 10 years of followup, indicating a safer and more durable effect than 1G-DES.

Background: Haemophilus influenzae is the causative pathogen for various infectious diseases, such as respiratory infections, otitis media, sinusitis, and meningitis. This study aimed to investigate the prevalence and molecular characteristics of β-lactam resistance in non-typeable H. influenzae isolates in South Korea. Methods: In total, 115 non-duplicated H. influenzae isolates were included in this study.Bacterial identification and serotyping were performed using matrix assisted laser desorption ionization-time of flight mass spectrometry and polymerase chain reaction (PCR) of bexA, respectively. Antimicrobial susceptibility was tested using the broth microdilution method.The production of β-lactamase was determined using nitrocefin disks. The presence of blaTEM and blaROB was confirmed using PCR. ftsI was analyzed to identify amino acid mutations in penicillin-binding protein (PBP) 3. Results: Resistance rates to ampicillin, amoxicillin–clavulanate, and cefuroxime were 67.8%, 13.9%, and 32.2%, respectively. None of the isolates were resistant to cefotaxime or ceftriaxone. Among 78 ampicillin-resistant isolates, 71 were β-lactamase-producing ampicillinresistant (BLPAR), and 7 were β-lactamase-non-producing ampicillin-resistant. All BLPAR isolates carried blaTEM, and none carried blaROB. Among 16 amoxicillin–clavulanate-resistant isolates, 15 β-lactamase producers harbored blaTEM. Four to 7 PBP3 mutations per isolate were detected in all 16 non-β-lactamase-producing ampicillin-resistant or cephalosporinresistant isolates. Conclusion Beta-lactam resistance in non-typeable H. influenzae isolates is highly prevalent in South Korea, primarily because of blaTEM and various PBP3 mutations. Therefore, continuous monitoring of antimicrobial resistance rates and mechanisms in non-typeable H.influenzae is necessary.

Objective: This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. Methods: Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics—compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)—from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. Results: The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. Conclusion The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.

Objective: This study aimed to evaluate the therapeutic role of lymphadenectomy in patients surgically treated for clinically early-stage epithelial ovarian cancer (EOC). Methods: This retrospective, multicenter study included patients with clinically earlystage EOC based on preoperative abdominal-pelvic computed tomography or magnetic resonance imaging findings between 2007 and 2021. Oncologic outcomes and perioperative complications were compared between the lymphadenectomy and non-lymphadenectomy groups. Independent prognostic factors were determined using Cox regression analysis.Disease-free survival (DFS) was the primary outcome. Overall survival (OS) and perioperative outcomes were the secondary outcomes. Results: In total, 586 patients (lymphadenectomy group, n=453 [77.3%]; nonlymphadenectomy groups, n=133 [22.7%]) were eligible. After surgical staging, upstaging was identified based on the presence of lymph node metastasis in 14 (3.1%) of 453 patients.No significant difference was found in the 5-year DFS (88.9% vs. 83.4%, p=0.203) and 5-year OS (97.2% vs. 97.7%, p=0.895) between the two groups. Using multivariable analysis, lymphadenectomy was not significantly associated with DFS or OS. However, using subgroup analysis, the lymphadenectomy group with serous histology had higher 5-year DFS rates than did the non-lymphadenectomy group (86.5% vs. 74.4%, p=0.048; adjusted hazard ratio=0.281; 95% confidence interval=0.107–0.735; p=0.010). The lymphadenectomy group had longer operating time (p<0.001), higher estimated blood loss (p<0.001), and higher perioperative complication rate (p=0.004) than did the non-lymphadenectomy group. Conclusion In patients with clinically early-stage EOC with serous histology, lymphadenectomy was associated with survival benefits. Considering its potential harm,

Objective: This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. Methods: Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics—compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)—from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. Results: The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. Conclusion The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.

Objective: This study aimed to evaluate the therapeutic role of lymphadenectomy in patients surgically treated for clinically early-stage epithelial ovarian cancer (EOC). Methods: This retrospective, multicenter study included patients with clinically earlystage EOC based on preoperative abdominal-pelvic computed tomography or magnetic resonance imaging findings between 2007 and 2021. Oncologic outcomes and perioperative complications were compared between the lymphadenectomy and non-lymphadenectomy groups. Independent prognostic factors were determined using Cox regression analysis.Disease-free survival (DFS) was the primary outcome. Overall survival (OS) and perioperative outcomes were the secondary outcomes. Results: In total, 586 patients (lymphadenectomy group, n=453 [77.3%]; nonlymphadenectomy groups, n=133 [22.7%]) were eligible. After surgical staging, upstaging was identified based on the presence of lymph node metastasis in 14 (3.1%) of 453 patients.No significant difference was found in the 5-year DFS (88.9% vs. 83.4%, p=0.203) and 5-year OS (97.2% vs. 97.7%, p=0.895) between the two groups. Using multivariable analysis, lymphadenectomy was not significantly associated with DFS or OS. However, using subgroup analysis, the lymphadenectomy group with serous histology had higher 5-year DFS rates than did the non-lymphadenectomy group (86.5% vs. 74.4%, p=0.048; adjusted hazard ratio=0.281; 95% confidence interval=0.107–0.735; p=0.010). The lymphadenectomy group had longer operating time (p<0.001), higher estimated blood loss (p<0.001), and higher perioperative complication rate (p=0.004) than did the non-lymphadenectomy group. Conclusion In patients with clinically early-stage EOC with serous histology, lymphadenectomy was associated with survival benefits. Considering its potential harm,

Objective: This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. Methods: Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics—compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)—from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. Results: The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. Conclusion The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.

Objective: This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. Methods: Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics—compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)—from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. Results: The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. Conclusion The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.