An artificial intelligence application to predict prolonged dependence on mechanical ventilation among patients with critical orthopaedic trauma: an establishment and validation study.

Background: Prolonged dependence on mechanical ventilation is a common occurrence in clinical ICU patients and presents significant challenges for patient care and resource allocation. Predicting prolonged dependence on mechanical ventilation is crucial for improving patient outcomes, preventing ventilator-associated complications, and guiding targeted clinical interventions. However, specific tools for predicting prolonged mechanical ventilation among ICU patients, particularly those with critical orthopaedic trauma, are currently lacking.

Comparison of the effects of different teaching methods on the effectiveness of teaching neurology in China: a bayesian network meta-analysis and systematic review.

Background: In China, investigations into the efficacy of neurological clinical teaching try to ascertain the impacts of various teaching methods on intervention outcomes. However, these studies often suffer from limited sample sizes, single-center studies and low quality, compounded by the lack of direct comparative analyses between teaching methods, thereby leaving the identification of the most effective method unresolved. This study aims to compare the effectiveness of various teaching methods in the standardized training of Chinese neurology clinicians to inform an optimal teaching model utilizing a Bayesian network meta-analysis (NMA) approach.

CT-3D MERGE fusion imaging improves image quality compared with CT and 3D MERGE in patients with lumbar disc herniation.

Background: CT-routine MRI fusion imaging has recently become available to evaluate spinal anatomy before surgery. Due to the 3-5 mm slice thickness and non-isotropic of routine MRI sequence, the CT-routine MRI fusion imaging is not good. The MRI multiple recalled gradient echo (MERGE) sequence is potentially useful in diagnosis of lumbar degeneration disease due to the better nerve roots visualization, 1 mm slice thickness and its isotropy.

A dataset of income distribution on provincial, urban, and rural levels for China from 2020 to 2100.

Projections of future income distributions at subnational levels are becoming increasingly important for a variety of analyses and evaluations. However, relevant datasets are currently limited. This study presents a methodological framework that introduces machine learning algorithms to a top-down approach used for generating income distribution datasets.

Oral microbiota as a biomarker for predicting the risk of malignancy in indeterminate pulmonary nodules: a prospective multicenter study.

Background: Determining the benign or malignant status of indeterminate pulmonary nodules (IPN) with intermediate malignancy risk is a significant clinical challenge. Oral microbiota-lung cancer interactions have qualified oral microbiota as a promising non-invasive predictive biomarker in IPN.

Materials And Methods: Prospectively collected saliva, throat swabs, and tongue coating samples from 1040 IPN patients and 70 healthy controls across three hospitals.