Indication model for laparoscopic repeat liver resection in the era of artificial intelligence: machine learning prediction of surgical indication.

Background: Laparoscopic repeat liver resection (LRLR) is still a challenging technique and requires a careful selection of indications. However, the current difficulty scoring system is not suitable for selecting indications. The purpose of this study is to develop the indication model for LRLR using machine learning and to identify factors associated with open conversion (OC).

Spatial distribution and activation changes of T cells in pancreatic tumors according to KRAS mutation subtype.

To enhance immunotherapy efficacy in pancreatic cancer, it is crucial to characterize its immune landscape and identify key factors driving immune alterations. To achieve this, we quantitatively analyzed the immune microenvironment using multiplex immunohistochemistry, assessing the spatial relationships between immune and tumor cells to correlate with patient survival rates and oncological factors. Additionally, through Whole Exome Sequencing analysis based on public data, we explored genetic mutations that could drive these compositions.

Disclosure of Adverse Events in Pediatrics: Policy Statement.

Disclosure of adverse events has become the expectation in medicine and is widely regarded as the appropriate path when medical errors occur. Although data are limited on adverse events in pediatrics, that they occur frequently is uncontested. Types and rates of errors vary depending on the care setting and patient population.

Factors influencing satisfaction with medical services in medically underserved populations: an analytical cross-sectional study at a free medical clinic in the Republic of Korea.

Objectives: This study aimed to explore factors influencing satisfaction with medical services among medically underserved populations at the free medical clinic, providing data to improve free medical services for these populations.

Methods: We employed a descriptive correlational study design involving 112 individuals (aged 19 years and older) from medically underserved populations who visited the clinic. Data were collected through face-to-face surveys from September to October 2023, and statistical analyses (t-tests, analysis of variance, Pearson correlation, and hierarchical multiple regression) were used to identify key predictors of satisfaction.

Polygenic risk scores for rheumatoid arthritis and idiopathic pulmonary fibrosis and associations with RA, interstitial lung abnormalities, and quantitative interstitial abnormalities among smokers.

Objective: Genome-wide association studies (GWAS) facilitate construction of polygenic risk scores (PRSs) for rheumatoid arthritis (RA) and idiopathic pulmonary fibrosis (IPF). We investigated associations of RA and IPF PRSs with RA and high-resolution chest computed tomography (HRCT) parenchymal lung abnormalities.

Methods: Participants in COPDGene, a prospective multicenter cohort of current/former smokers, had chest HRCT at study enrollment.