Background: and Purpose: Tailored physical exercise interventions have the potential to promote cognitive health in older adults and offer significant advantages for those more vulnerable to decline. The specific relationship between physical fitness and cognition among the elderly has not been clearly established. The purpose of this investigation was to assess the relationship between physical fitness and cognitive function in older Korean adults. Methods: Eighty-four community-dwelling older adults (mean age: 70.7±5.3 years; 81.0% female) completed a standardized physical fitness battery assessing handgrip strength, sit-and-reach, 30-second sit-to-stand, 2-minute stationary march, 3-m sit-walk-and-return, figure-8-walk, and T-wall response time. Cognitive function was evaluated using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Descriptive statistics, partial correlation analyses, and stepwise multiple linear regression were conducted. Results: Slower T-wall response time was significantly correlated with lower RBANS total index, immediate memory, and delayed memory scores. In regression models, slower T-wall response time was independently associated with lower RBANS total index (β=−0.234, p=0.026) and delayed memory scores (β=−0.295, p=0.029). The regression model for immediate memory was not statistically significant overall; therefore, no predictive conclusion was drawn for this domain. Higher education showed a significant positive association with cognitive performance. Conclusions Coordination, as measured by T-wall response time, emerged as the only physical fitness component consistently associated with cognitive performance in older adults. Coordination-related fitness may be an important correlate of cognitive function in older adults and a promising target for future exercise interventions.

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.

Purpose: The number of homebound adults with serious illnesses is increasing. This study aimed to examine the healthcare needs and characteristics of patients who use a hospital-affiliated physician-led home-based medical care (HBMC) program and identify factors associated with emergency department (ED) visits in Korea. Materials and Methods: This retrospective observational study included patients who used a HBMC program at a tertiary hospital between 2020 and 2023. Patient characteristics and home healthcare needs were analyzed by disease category: cancer, advanced neurologic disease, and others. Multivariable logistic regression analysis was used to identify factors associated with ED visits within 30 days of a physician’s home visit. Results: A total of 600 patients were registered and received home visits; 58.5% had cancer and 29.7% had advanced neurologic diseases, e.g., amyotrophic lateral sclerosis. The median age was 72 years [interquartile range (IQR), 62.8–81.0], and 87.0% were dependent in daily activities. The median number of medications per patient was 6 (IQR, 3–10); 66.3% took ≥5 medications and 25.7% took ≥10 (excessive polypharmacy). Physicians provided not only physical examinations (100%) and symptom assessment (90.8%), but also home environment evaluation (86.7%), medical device management (62.0%), advanced care planning (40.7%), and acute health issue management (32.5%). Within 30 days, 19.2% of patients visited the ED. Excessive polypharmacy and cancer diagnosis were associated with increased ED visits. Conclusion Most patients who used the hospital-affiliated HBMC program had cancer, advanced neurologic disease, and polypharmacy. Targeted HBMC programs are needed for patients with serious illnesses living at home.

Background and Objectives: Clinical characteristics and outcomes in acute heart failure (AHF) vary by phenotype. We assessed phenotype-specific features, treatment patterns, and outcomes in a nationwide Korean cohort. Methods: The Korean Heart Failure III registry prospectively enrolled 7,351 AHF admissions at 47 hospitals. Among 6,777 patients with available left ventricular ejection fraction (EF), phenotypes were defined as heart failure with reduced EF (HFrEF, ≤40%), mildly reduced EF (HFmrEF,41–49%), or preserved EF (HFpEF, ≥50%). The primary endpoint was a 12-month composite of all-cause death or heart transplantation, evaluated from index admission and, among hospital survivors, from discharge. We used inverse probability weighting (multinomial generalized boosted models with stabilized, trimmed weights) and weighted Cox proportional-hazards models to estimate hazard ratios (HRs). Results: Phenotype distribution was 58.9% HFrEF, 13.6% HFmrEF, and 27.5% HFpEF. Crude 12-month composite rates from index admission were 13.4% (HFrEF), 12.7% (HFmrEF), and 16.8% (HFpEF). After weighting, from index admission, HFmrEF (HR, 0.892; 95% confidence interval [CI], 0.731–1.088) and HFpEF (HR, 1.101; 95% CI, 0.939–1.291) did not differ from HFrEF; from discharge, HFpEF had modestly higher risk (HR, 1.207; 95% CI, 1.008–1.445) whereas HFmrEF did not (HR, 1.039; 95% CI, 0.844–1.279). Hyponatremia and chronic kidney disease were consistent adverse markers, while angiotensin-converting enzyme inhibitor/ angiotensin II receptor blocker use at discharge was protective. Conclusions Across the EF spectrum, phenotypes showed distinct profiles and risk. Postdischarge risk was modestly higher in HFpEF, supporting phenotype-tailored care and systematic discharge optimization in Korean patients with AHF.

Heart failure (HF) prevalence and associated socio-economic costs have risen rapidly in Korea, creating urgent demand for a national, standardized prospective registry to support quality monitoring, outcome comparison, and real-world evidence. Standardized data definitions are essential for valid comparisons across studies and healthcare settings. We created a unified national HF cohort by integrating the two largest Korean registries, KorAHF (2011–2014;n=5,625) and KorHF III (2018–2023; n=7,351), yielding 12,976 hospitalized HF patients. Because heterogeneous variable definitions impeded direct comparisons, we implemented a harmonization pipeline across demographic, clinical, imaging, biomarker, therapeutic, and outcome domains, aligned with international standards and reinforced by systematic data quality checks.A multidisciplinary panel then conducted a structured decision process to produce the Korean HF Standard Dataset, a tiered and interoperable variable set spanning six domains: demographics, presentation at admission, in-hospital events and comorbidities, discharge status, outpatient follow-up, and readmission events. This integrated platform and standardized variables enable precise identification of HF patient phenotypes and prognostic factors, establishing an evidence-based foundation for disease management. This supports an analysis of temporal changes in treatment patterns and outcomes, generates data to inform future clinical practice guidelines, and provides a practical basis for planning large, continuous, prospective HF registries. Furthermore, it also facilitates pragmatic, registry-based randomized clinical trials and unmet-needs assessments that can guide national health policy. Establishing a foundation for future linkage with national health databases, this platform will allow for enhanced accuracy, comparability, and representativeness, powering quality improvement initiatives and population-level monitoring of Korea’s growing HF burden.

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: Dual energy X-ray absorptiometry (DXA) measurements vary among instruments from different manufacturers. This study aimed to assess the correlation between bone mineral density (BMD) and body composition measured by the GE Lunar Prodigy and Hologic Horizon W DXA systems and to develop conversion equations. Methods: A total of 120 healthy volunteers (59 males, 61 females) aged 20 to 70 years were scanned on both DXA systems on the same day. BMD was measured at the lumbar spine (L1-L4) and bilateral femurs. Body composition parameters were measured for the whole body and regional sites. Bland-Altman analysis, Pearson correlation, and concordance correlation coefficient (CCC) were used to assess agreement. Linear regression was performed to derive conversion equations. Results: The Hologic system measured BMD values 14% to 20% lower at the lumbar spine and 8% to 17% lower at femoral sites compared to the GE Lunar system (all P<0.0001). Lean mass values were 4% to 10% lower on the Hologic system. Very strong correlations were observed for both BMD (r=0.944-0.980) and lean mass (r=0.963-0.984). Cross-calibration equations were developed for all measurement sites. Conclusions Despite significant systematic differences, there was very high correlation between the two DXA systems. The conversion equations can facilitate comparison of measurements between these systems in clinical practice and research.

Purpose: Based on the KEYNOTE-522 study, neoadjuvant pembrolizumab plus chemotherapy has become the standard treatment for early-stage triple-negative breast cancer (TNBC).This study evaluated the real-world efficacy, safety, and predictors of pathologic complete response (pCR) in Korean patients. Methods: We conducted a retrospective cohort study of 174 patients with early-stage TNBC who received the KEYNOTE-522 regimen (neoadjuvant pembrolizumab plus paclitaxel and carboplatin, followed by doxorubicin and cyclophosphamide) at a tertiary cancer center between August 2022 and July 2024. We assessed the primary endpoints, including pCR rate and event-free survival (EFS). We performed univariable and multivariable logistic regression analyses to identify independent predictors of pCR. Results: The median patient age was 50 years (range, 24–74 years). The clinical stages were II and III in 79.3% and 20.1% of patients, respectively, and 10.9% had clinical N3 disease. The overall pCR rate was 62.1%, and the N3 subgroup had a pCR rate of 47.4%. On multivariable analysis, high baseline Ki-67 expression (≥ median, 75%) was significantly associated with pCR (odds ratio, 2.84; 95% confidence interval, 1.45 to 5.66; p = 0.002). At a median followup of 18.4 months, the 12-month EFS rate was 97.4%, with significantly superior outcomes observed in patients who achieved pCR compared with those who did not achieve pCR (100% vs. 93.1%, p = 0.007). The treatment completion rate was 92.0%, and immune-related adverse events occurred in 13.8% of patients. Conclusion In this real-world analysis of one of the largest Asian cohorts of patients with earlystage TNBC treated with neoadjuvant pembrolizumab, the KEYNOTE-522 regimen demonstrated substantial efficacy and manageable toxicity, consistent with the original trial findings.

Reference management software (RMS) represents a cornerstone of modern academic writing and publishing. For decades, programs such as EndNote, Zotero, and Mendeley have played central roles in facilitating citation organization, bibliography formatting, and collaborative scholarship. Although each platform has introduced unique innovations, persistent limitations remain, particularly with respect to usability, accessibility, and accuracy. In parallel, the rise of generative artificial intelligence has introduced an unprecedented challenge: the inadvertent inclusion of fabricated or incorrect references mistakenly incorporated into manuscripts. This phenomenon has exposed a critical limitation of traditional RMS platforms, namely their inability to verify reference authenticity. Against this backdrop, new solutions have emerged. One such example is CiteWell (https://citewell.org/), an artificial intelligence (AI)-era RMS that introduces several notable innovations, including PubMed-integrated verification, an intuitive interface for new users, customizable journal-specific styles, and multilingual accessibility. This review provides a comprehensive historical overview of RMS, evaluates the strengths and weaknesses of major platforms, and positions emerging AI-based tools as a new paradigm that combines traditional reference management with essential safeguards for contemporary academic challenges.