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Objectives: The aim of this study was to assess the quality of tuberculosis (TB) care for the whole course and assess factors that affect completing treatment., Design: This is an observational retrospective study using chart abstraction for the whole course of TB care conducted at two underserved provinces in China., Setting: The study was conducted from June 2021 to July 2021. All medical records (outpatient and inpatient) for the whole course (6-8 months) of patients with TB newly registered from July 2020 to December 2020 were reviewed and abstracted using predetermined checklists., Participants: A total of 268 outpatient medical records and 126 inpatient records were included., Primary and Secondary Outcome Measures: The primary outcome included diagnostic quality, treatment quality and management quality. The secondary outcome was completing treatment., Results: For diagnostic quality, 94.2% of the diagnosis were based on adequate evidence. For treatment quality, 240 (91.6%) outpatients and 100 (85.5%) inpatients took the standard chemotherapy regimens. 234 (87.3%) patients completed treatment. 85.1% of the inpatients prescribed with second-line drugs were inappropriate. For management quality, 128 (47.9%) patients received midterm assessments, but only 47 (19.7%) received sufficient services for the whole course. Patients with TB symptoms were 1.8 times more likely to complete treatment (p=0.011)., Conclusion: Patients with TB received high-quality diagnosis and treatment services, but low-quality whole-course management. Integration of medical and public health services should be strengthened to improve whole-course quality., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objective: The accurate diagnosis of superficial lymphadenopathy is challenging. We aim to explore a non-invasive and accurate machine-learning method for distinguishing benign lymph nodes, lymphoma, and metastatic lymph nodes., Methods: The clinical data and ultrasound images of 160 patients with superficial lymphadenopathy (58 benign lymph nodes, 62 lymphoma, 40 metastatic lymph nodes) admitted to our hospital from January 2020 to November 2022 were retrospectively studied. Patients were randomly divided into a training set and test set according to the ratio of 6:4. Firstly, the radiomics features of each lymph node were extracted, and then a series of statistical methods were used to avoid over-fitting. Then, the gradient boosting machine(GBM) was used to build the model. The area under receiver(AUC) operating characteristic curve, precision, recall rate and F1 value were calculated to evaluate the effectiveness of the model., Results: Ten robust features were selected to build the model. The AUC values of benign lymph nodes, lymphoma and metastatic lymph nodes in the training set were 1.00, 0.98 and 0.99, and the AUC values of the test set were 0.96, 0.84 and 0.90, respectively., Conclusion: It was a reliable and non-invasive method for the differential diagnosis of lymphadenopathy based on the model constructed by machine learning., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)