Autoimmune hemolytic anemia (AIHA) is a rare immune-mediated disorder in children that can present as primary or secondary to other diseases. Here, we report an unusual case of steroid-refractory warm AIHA in an 11-year-old girl whose condition was ultimately cured after removal of a benign ovarian tumor. Despite receiving multiple lines of therapy—including corticosteroids, rituximab, cyclosporine, sirolimus, and mycophenolate mofetil—the patient experienced recurrent hemolysis and steroid dependence for nearly four years. Abdominopelvic imaging performed to evaluate fever revealed bilateral ovarian cystic lesions, including a left-sided dermoid cyst. Surgical resection of the tumor led to complete and sustained hematologic remission, with normalization of hemoglobin, bilirubin, and reticulocyte counts, allowing discontinuation of all immunosuppressive agents. No recurrence of hemolysis was observed during 18 months of follow-up. This case highlights the potential for benign ovarian tumors to act as a rare secondary cause of AIHA through paraneoplastic or immune cross-reactive mechanisms. Awareness of such associations is crucial when evaluating pediatric patients with refractory or relapsing AIHA, as identification and removal of an occult tumor may achieve definitive resolution of hemolysis and avoid long-term immunosuppression.

Secondary rhabdoid tumors (RTs) with atypical teratoid/rhabdoid tumor-like features rarely arise from, or coexist with, pleomorphic xanthoastrocytomas (PXAs), and their clinicopathological and molecular characteristics remain poorly understood. We report a 17-year-old girl with a temporal lobe mass that, upon gross total resection, pathologically contained both RT and PXA components. Immunohistochemistry revealed loss of INI1 expression restricted to the RT component, while the PXA area retained INI1. Next-generation sequencing identified a shared BRAF::TRIM24 fusion and homozygous deletion of CDKN2A/2B in both components, indicating a shared clonal origin. Additionally, a germline ATM frameshift mutation (c.5288_5289insGA) was identified in both tumor components, making the first such report in central nervous system tumors. SMARCB1 loss was confined to the RT component, further supporting the hypotheses of clonal evolution and secondary transformation. Despite gross total resection, craniospinal irradiation, and chemotherapy, the patient developed rapid leptomeningeal dissemination and died 5 months after surgery. This case provides clinicopathological and molecular evidence for clonal evolution and secondary transformation of PXA into an RT. The presence of germline ATM mutation may have therapeutic and biological relevance. Further studies are required to clarify the pathogenesis and optimal management of these rare and aggressive tumors.

Objectives: Large language models (LLMs) demonstrate remarkable potential in healthcare communication. However, whether they can process complex, high-volume medical information, such as stroke-related content, remains insufficiently validated. This study aimed to evaluate the natural language processing capabilities of LLMs in handling such content and to develop an evaluation instrument. Methods: A survey compared educational materials generated by two LLMs (ChatGPT 4.0 and Claude 3) with neurologist-authored content on stroke. The materials were based on two clinical scenarios representing distinct stroke etiologies: cardioembolism and large-artery atherosclerosis. They were evaluated in terms of accuracy, legality, ethics, comprehensiveness, and information delivery. Scores for comprehensiveness and information delivery were compared according to participants’ agreement with the use of LLMs in healthcare. Results: ChatGPT received the highest scores across all domains, except for legality in Scenario 2. In Scenario 1, the ranking for accuracy and summarization of clinical information was, from highest to lowest, ChatGPT, Claude, and the neurologist (η2 = 0.140, p < 0.001; η2 = 0.175, p < 0.001). The same hierarchy was observed in Scenario 2 for accuracy (η2 = 0.077, p < 0.001) and summarization (η2 = 0.194, p < 0.001). Participants who agreed with the use of LLMs in healthcare assigned higher scores for the comprehensiveness (Scenario 1, p = 0.005; Scenario 2, p = 0.007) and information delivery (Scenario 1, p = 0.003; Scenario 2, p = 0.026) of ChatGPT-generated materials than participants who did not agree. Conclusions LLMs demonstrated adequate capability to convey complex content, such as stroke-related information, in an accessible and understandable manner for non-experts.

The sixth edition of the Korean Medication Algorithm Project for Depressive Disorder (KMAP-DD) was published in 2025. This review compared KMAP-DD 2025 with four major international clinical practice guidelines: Canadian Network for Mood and Anxiety Treatments Clinical Guidelines for the Management of Major Depressive Disorders, National Institute for Health and Care Excellence Depression Guideline, Royal Australian and New Zealand College of Psychiatrists Clinical Practice Guidelines for Mood Disorders, and British Association for Psychopharmacology Guideline. While KMAP-DD is based on expert consensus, and others on evidence-based methods, overall treatment strategies for depressive episodes were fairly consistent. Especially, KMAP-DD 2025 offers more structured recommendations in areas lacking strong evidence, such as premenstrual dysphoric disorder, perinatal depression, and depression with medical comorbidities. KMAP-DD 2025 also reflected Korean clinical practice patterns emphasizing rapid symptom relief and early use of combination strategies. Despite limitations as a consensus-based guideline, KMAP-DD 2025 complements evidence-based approaches and provides practical, situation-specific guidance for real-world clinical decision-making in Korea.

In patients with differentiated thyroid cancer (DTC), initial recurrence risk stratification based on clinical, histopathological, and perioperative data remains the key determinant for guiding management strategies during the first 1-2 years post-treatment. However, the adoption of ongoing risk stratification (ORS), which dynamically reassesses risk by integrating longitudinal clinical data and treatment response, enables more precise long-term prognostic assessment and facilitates highly individualized management. Building upon recent guidelines, the 2026 KTA guideline has been further refined by incorporating robust evidence from large-scale national cohorts and comprehensive systematic reviews. These updated recommendations outline contemporary concepts of ORS, risk-adapted TSH suppression targets, optimized surveillance modalities for recurrence detection, and disease-specific long-term follow-up strategies. Reflecting the paradigm shift toward de-escalated treatment, this revision integrates evolved perspectives on TSH suppression intensity, the clinical interpretation of thyroglobulin levels, and tailored follow-up intervals. These evidence-based recommendations aim to minimize unnecessary treatment and excessive surveillance in the large proportion of patients with excellent prognosis after initial therapy, while ensuring that each patient receives appropriately tailored and effective long-term management.

Background: Left bundle branch block (LBBB) significantly increases the risk of left ventricular systolic dysfunction (LVSD) due to cardiac dyssynchrony. Although artificial intelligence–enabled electrocardiography (AI-ECG) mod‑ els show promise in detecting LVSD, their performance in LBBB patients remains underexplored. We hypothesized that an AI-ECG model clinically validated for detecting LVSD would accurately detect LVSD and predict future clinical outcomes in LBBB patients. Methods: In this retrospective multicenter study, 5,689 expert-validated LBBB ECGs collected from 2,813 patients between 2016 and 2024 were analyzed using a previously developed and validated AI-ECG model. LVSD was defined as an ejection fraction of ≤ 40%. Model performance was assessed using the area under the receiver operating char‑ acteristic curve (AUROC), the area under the precision-recall curve (AUPRC), sensitivity, and specificity. Patients were stratified into high- and low-risk groups based on a threshold that achieved 90% sensitivity. A Kaplan–Meier analysis was used to compare clinical outcomes. Results: Among the 2,813 LBBB patients (mean age, 70.7 years; male sex, 43.7%), hypertension and a history of heart failure were common. The AiTiALVSD model showed strong diagnostic performance for LVSD (AUROC, 0.930 [95% CI, 0.924–0.937]; AUPRC, 0.913 [95% CI, 0.902–0.923]; sensitivity, 0.979; specificity, 0.473). During the mean follow-up of 4.1 years, high-risk patients had significantly higher hazards than low-risk patients for all-cause mortality (adjusted hazard ratio [HR], 1.87; 95% CI, 1.53–2.28), implantable cardioverter defibrillator/cardiac resynchronization therapy implantation (adjusted HR, 15.2; 95% CI, 7.51–30.77), and cardiovascular hospitalization (adjusted HR, 1.11; 95% CI, 0.96–1.28). Conclusions AiTiALVSD effectively detects LVSD and stratifies long-term cardiovascular risk in LBBB patients, support‑ ing its clinical utility for early detection and patient management.

Abrupt vessel closure (AVC) is a complication of percutaneous coronary intervention (PCI) and is defined as the sudden cessation of forward coronary blood flow due to a variety of mechanisms. The incidence of AVC has markedly decreased from approximately 3.0% in the early era to about 0.3% today. Nevertheless, cardiovascular interventionists must remain prepared to manage AVC effectively, given its significant impact on clinical outcomes.AVC can arise from several mechanisms, including dissection, intramural hematoma, intracoronary thrombosis, air embolism, vasospasm, and no-reflow. The clinical impact of AVC varies depending on the extent of myocardium affected, ranging from asymptomatic events or transient ischemic chest pain to cardiogenic shock or cardiac arrest. Both a general approach for stabilizing hemodynamic consequences and an etiology-specific management strategy are essential. Hasty decision-making may worsen the situation, whereas a structured approach—focused on hemodynamic stabilization, logical diagnostic processes, and targeted interventions—can improve outcomes. In this narrative review, we describe the mechanisms underlying each etiology, outline mechanism-specific management strategies, and discuss general approaches to managing patients with AVC through logical diagnostic and treatment processes. We hope this review offers valuable insights into this uncommon but significant complication and enhances readers’ skills in managing AVC more effectively during PCI.

The coronavirus disease 2019 (COVID-19) pandemic has substantially influenced nursing environments and has underscored the importance of active patient care. Nursing intention plays a critical role in the provision of care for patients with COVID-19; however, limited evidence exists regarding the factors that influence nurses’ intention to provide patient care specific to COVID-19. This study aimed to examine factors associated with nursing intention for COVID-19 patient care, guided by the Theory of Planned Behavior. Methods: This study employed a cross-sectional descriptive design. Data were collected from August 30 to October 1, 2020, using structured questionnaires completed by 169 clinical nurses working in hospitals with more than 600 beds in Seoul, Korea. The collected data were analyzed using multiple regression analysis. Results: Behavioral beliefs (β=.34, p<.001) and perceived behavioral control (β=.24, p<.001) were significantly associated with nursing intention for COVID-19 patient care. Conclusion: There is a need to develop specific strategies and educational programs to enhance nursing intention for COVID-19 patient care in the context of emerging infectious diseases by strengthening behavioral beliefs and perceived behavioral control. Providing up-to-date care protocols or simulation-based education may help increase nurses’ behavioral beliefs and perceived behavioral control.