Pang, Maoyin

ABSTRACT Following a routine screening colonoscopy, a 54-year-old man underwent colon resection for adenocarcinoma, which revealed neoplastic B-cells in the lymph nodes surrounding the colon suggestive of chronic lymphocytic leukemia (CLL). Further evaluation identified a monoclonal B-cell population, and he was diagnosed with monoclonal B-cell lymphocytosis without meeting CLL diagnostic criteria. The patient had no clinical signs of leukemia and did not need acute treatment but was placed under annual surveillance. This case highlights the incidental discovery of neoplastic B-cells, emphasizing the importance of comprehensive histopathological evaluation and clinical correlation to guide management and monitoring for potential progression to CLL.

INTRODUCTION: This study investigates seasonal variations in eosinophilic esophagitis (EoE)-related emergency department visits among adults. METHODS: We analyzed the National Emergency Department Sample (2016–2021), identifying adult patients with EoE using ICD-10 codes. Generalized additive models assessed seasonal patterns. RESULTS: Among 18,791 EoE-related emergency department visits, a significant seasonal variation was observed, peaking in summer and nadiring in winter. This pattern was consistent across all US regions. CONCLUSION: Seasonal dietary habits and social behaviors likely contribute to EoE exacerbations. Healthcare providers should emphasize management strategies during high-risk periods, particularly summer months and weekends.

AbstractBackground and AimEsophageal cancer significantly contributes to US cancer mortality, with notable racial disparities. This study aims to provide updated esophageal cancer mortality trends among Black and White adults from 1999 to 2020.MethodsCDC‐WONDER was used to identify Black and White adults in the United States from 1999 to 2020. We calculated age‐standardized mortality rates, absolute rate differences, and rate ratios to compare the mortality differences between these populations.ResultsFrom 1999 to 2020 in the United States, there were 303 267 esophageal cancer deaths, with significant racial disparities. The age‐adjusted mortality rate for Black adults fell from 6.52 to 2.62 per 100 000, while for White adults, it declined from 4.19 to 3.97 per 100 000, narrowing the racial mortality gap. Gender‐wise, the study showed a decrease in the mortality rate from 3.31 to 2.29 per 100 000 in Black women, but an increase from 1.52 to 1.99 per 100 000 in White women. Among young men, the rate dropped in Black men from 12.82 to 6.26 per 100 000 but rose in White men from 9.90 to 10.57 per 100 000. Regionally, Black adults in the Midwest and South initially had higher mortality rates than Whites, but this gap reduced over time. By 2020, Black men had lower mortality rates across all regions.ConclusionsOver the last two decades, age‐adjusted esophageal cancer mortality decreased in Black adults but stabilized in White adults, reflecting distinct cancer trends and risk factors. The study highlights the importance of tailored public health strategies for healthcare access and risk factor management.

ABSTRACT Airway stenting has become integral to the therapeutic endoscopic management of benign and malignant obstructive airway diseases. Despite the increased use of stents, the absence of clear guidelines for surveillance and maintenance poses the potential for unique stent-associated complications. Our case reports a rare incident of tracheal stent dislodgement, leading to its ingestion and unexpected discovery within the stomach. This case serves the purpose of shedding light on a rare yet potentially life-threatening complication and discussing types of stent and characteristics to enhance gastroenterologists' understanding of stent-related challenges and equips them to anticipate and strategize the appropriate course of action.

ABSTRACT Severe acute pancreatitis can cause systemic inflammation and multiorgan failure. We present the case of a 60-year-old woman who presented with necrotizing pancreatitis and subsequently developed a sigmoid colon perforation. The perforation presumably occurred because of extravasation of pancreatic enzymes into the abdominal cavity, resulting in colonic wall injury. Our case highlights the rare colonic complications of severe acute pancreatitis.

AbstractBackground and AimEffective clinical event classification is essential for clinical research and quality improvement. The validation of artificial intelligence (AI) models like Generative Pre‐trained Transformer 4 (GPT‐4) for this task and comparison with conventional methods remains unexplored.MethodsWe evaluated the performance of the GPT‐4 model for classifying gastrointestinal (GI) bleeding episodes from 200 medical discharge summaries and compared the results with human review and an International Classification of Diseases (ICD) code‐based system. The analysis included accuracy, sensitivity, and specificity evaluation, using ground truth determined by physician reviewers.ResultsGPT‐4 exhibited an accuracy of 94.4% in identifying GI bleeding occurrences, outperforming ICD codes (accuracy 63.5%, P < 0.001). GPT‐4's accuracy was either slightly lower or statistically similar to individual human reviewers (Reviewer 1: 98.5%, P < 0.001; Reviewer 2: 90.8%, P = 0.170). For location classification, GPT‐4 achieved accuracies of 81.7% and 83.5% for confirmed and probable GI bleeding locations, respectively, with figures that were either slightly lower or comparable with those of human reviewers. GPT‐4 was highly efficient, analyzing the dataset in 12.7 min at a cost of 21.2 USD, whereas human reviewers required 8–9 h each.ConclusionOur study indicates GPT‐4 offers a reliable, cost‐efficient, and faster alternative to current clinical event classification methods, outperforming the conventional ICD coding system and performing comparably to individual expert human reviewers. Its implementation could facilitate more accurate and granular clinical research and quality audits. Future research should explore scalability, prompt and model tuning, and ethical implications of high‐performance AI models in clinical data processing.