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1.
Langenbecks Arch Surg ; 409(1): 148, 2024 May 02.
Artículo en Inglés | MEDLINE | ID: mdl-38695994

RESUMEN

In the past 40 years, the incidence of esophagogastric junction cancer has been gradually increasing worldwide. Currently, surgical resection remains the main radical treatment for early gastric cancer. Due to the rise of functional preservation surgery, proximal gastrectomy has become an alternative to total gastrectomy for surgeons in Japan and South Korea. However, the methods of digestive tract reconstruction after proximal gastrectomy have not been fully unified. At present, the principal methods include esophagogastrostomy, double flap technique, jejunal interposition, and double tract reconstruction. Related studies have shown that double tract reconstruction has a good anti-reflux effect and improves postoperative nutritional prognosis, and it is expected to become a standard digestive tract reconstruction method after proximal gastrectomy. However, the optimal anastomoses mode in current double tract reconstruction is still controversial. This article aims to review the current status of double tract reconstruction and address the aforementioned issues.


Asunto(s)
Anastomosis Quirúrgica , Gastrectomía , Procedimientos de Cirugía Plástica , Neoplasias Gástricas , Humanos , Gastrectomía/métodos , Neoplasias Gástricas/cirugía , Neoplasias Gástricas/patología , Anastomosis Quirúrgica/métodos , Procedimientos de Cirugía Plástica/métodos , Unión Esofagogástrica/cirugía , Colgajos Quirúrgicos , Yeyuno/cirugía
2.
Am J Otolaryngol ; 45(3): 104204, 2024.
Artículo en Inglés | MEDLINE | ID: mdl-38181649

RESUMEN

OBJECTIVE: To establish a nasopharyngeal carcinoma-specific big data platform based on electronic health records (EHRs) to provide data support for real-world study of nasopharyngeal carcinoma. METHODS: A multidisciplinary expert team was established for this project. Based on industry standards and practical feasibility, the team designed the nasopharyngeal carcinoma data element standards including 14 modules and 640 fields. Data from patients diagnosed with nasopharyngeal carcinoma who visited Southern Hospital after 1999 were extracted from 15 EHRs systems and were cleaned, structured, and standardized using information technologies such as machine learning and natural language processing. In addition, a series of measures such as quality control and data encryption were taken to ensure data quality and patient privacy. At the platform application level, 10 functional modules were designed according to the needs of nasopharyngeal carcinoma research. RESULTS: As of 1 October 2022, the Big Data platform has included 11,617patients, of whom 8228 (70.83 %) were male and 3389 (29.17 %) were female, with a median age of 48 years (interquartile range, 40 years). The data in the platform were validated to have a high level of completeness and accuracy, especially for key variables such as social demographics, laboratory tests and vital signs. Currently, six projects involving risk factors, early diagnosis, treatment efficacy and prevention of treatment-related toxic reactions have been conducted on the platform. CONCLUSIONS: We have established a high-quality NPC-specific big data platform by integrating heterogeneous data from multiple sources in the EHR. The platform provides an effective tool and strong data support for real-world studies of nasopharyngeal carcinoma, which helps to improve research efficiency, reduce costs, and improve the quality of research results. We expect to promote multicenter nasopharyngeal carcinoma data sharing in the future to facilitate the generation of high-quality real-world evidence in nasopharyngeal carcinoma. This article may provide some reference value for other comprehensive hospitals to establish a big data platform for nasopharyngeal carcinoma.


Asunto(s)
Macrodatos , Registros Electrónicos de Salud , Carcinoma Nasofaríngeo , Neoplasias Nasofaríngeas , Humanos , Carcinoma Nasofaríngeo/terapia , Carcinoma Nasofaríngeo/diagnóstico , Masculino , Femenino , Persona de Mediana Edad , Adulto , Aprendizaje Automático , Procesamiento de Lenguaje Natural
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