ABSTRACT
Background: Epididymal cysts (ECs) are uncommon in the pediatric population. The objective of this study was to evaluate the frequency, clinical characteristics, and management strategies of ECs in children. Methods: We performed a retrospective review of pediatric scrotal ultrasounds between January 2014 and August 2022 to identify children with ECs. Results: One hundred and forty-three children boys were found to have ECs, with 95 being pre-pubertal and 48 post-pubertal. The age of the patients ranged from 1 day to 18 years, with a mean age of 10.64 ± 4.55 years. The size of the cysts varied from 2â mm to 35â mm. The most common comorbidities observed were hydrocele, testicular microlithiasis and varicocele. The majority of ECs were detected through routine physical examination. Conservative management was employed for all patients, except for one who required surgical excision. Resolution of ECs occurred in 12 patients, while a reduction in cyst size was observed in 6 cases. Conversely, 2 patients experienced an increase in cyst size, and 6 patients exhibited an increase in cyst number during the follow-up period. Conclusion: Conservative management is the preferred approach for the majority of cases, with surgical intervention reserved for specific instances.
ABSTRACT
Chinese medicine is a unique and complex medical system with complete and rich scientific theories. The textual data of Traditional Chinese Medicine (TCM) contains a large amount of relevant knowledge in the field of TCM, which can serve as guidance for accurate disease diagnosis as well as efficient disease prevention and treatment. Existing TCM texts are disorganized and lack a uniform standard. For this reason, this paper proposes a joint extraction framework by using graph convolutional networks to extract joint entity relations on document-level TCM texts to achieve TCM entity relation mining. More specifically, we first finetune the pre-trained language model by using the TCM domain knowledge to obtain the task-specific model. Taking the integrity of TCM into account, we extract the complete entities as well as the relations corresponding to diagnosis and treatment from the document-level medical cases by using multiple features such as word fusion coding, TCM lexicon information, and multi-relational graph convolutional networks. The experimental results show that the proposed method outperforms the state-of-the-art methods. It has an F1-score of 90.7% for Name Entity Recognization and 76.14% for Relation Extraction on the TCM dataset, which significantly improves the ability to extract entity relations from TCM texts. Code is available at https://github.com/xxxxwx/TCMERE.