Objective To summarize the development, clinical application, advantages and disadvantages, and future prospects of parathyroid autofluorescence in recent years. MethodThe literatures related to the research progress of parathyroid autofluorescence in recent years were searched, and launched a specific discussion. Results Autofluorescence of parathyroid gland was still in its infancy at home and abroad. The existing studies had shown that this technique was superior to visual recognition and could reduce the incidence of postoperative complications. Autofluorescence technology had shown some advantages in identifying parathyroid gland during operation, and its mechanism research and related equipment improvement should be focused in the future. ConclusionAutofluorescence technique is of great value in the identification of parathyroid glands in patients undergoing thyroidectomy or parathyroidectomy.
ObjectiveTo understand the methods of judging the blood supply of parathyroid during thyroidectomy at home and abroad in recent years. MethodThe literature on parathyroid blood supply was collected, the research progress was reviewed, and the advantages and disadvantages of related methods were analyzed. ResultsIn recent years, near-infrared fluorescence, laser speckle contrast imaging and other technologies had been applied. They showed better advantages as compared with naked eye observation. The research on parathyroid blood supply at home and abroad was still in its infancy, and more clinical samples and related equipment optimization were still needed. ConclusionFluorescence imaging technology has a certain auxiliary role in the judgment of intraoperative parathyroid blood supply and can reduce the incidence of hypoparathyroidism to a certain extent.
Objectiv To provide a comprehensive overview of model performance and predictive efficacy of machine learning techniques to predict septic shock in children, in order to target and improve the quality and predictive power of models for future studies. MethodsTo systematically review all studies in four databases (PubMed, Embase, Web of Science, ScienceDirect) on machine learning prediction of septic shock in children before April 1, 2024. Two investigators independently conducted literature screening, literature data extraction and bias assessment, and conducted a systematic review of basic information, research data, study design and prediction models. Model discrimination, which area under the curve (AUC), was pooled using a random-effects model and meta-analysis was performed. Subgroup analyses were performed according to sample sizes, machine learning models, types of predictors, number of predictors, etc. And publication bias and sensitivity analyses were performed for the included literature. Results A total of 11 studies were included, of which 2 were at low risk of bias, 7 were at unknown risk of bias, and 2 were at high risk of bias. The data used in the included studies included both public and non-public electronic medical record databases, and the machine learning models used included logistic regression, random forest, support vector machine, and XGBoost, etc. The predictive models constructed based on different databases appeared to have different results in terms of the characteristic variables, so identifying the key variables of the predictive models requires further validation on other datasets. Meta-analysis showed the pooled AUC of 0.812 (95%CI 0.763 to 0.860, P<0.001), and further subgroup analyses showed that larger sample sizes (≥1 000) and predictor variable types significantly improved the predictive effect of the model, and the difference in AUC was statistically significant (95%CI not overlapping). The funnel plot showed that there was publication bias in the study, and when the extreme AUC values were excluded, the meta-analysis yielded a total AUC of 0.815 (95%CI 0.769 to 0.861, P<0.001), indicating that the extreme AUC values were insensitive. ConclusionMachine learning technology has shown some potential in predicting septic shock in children, but the quality of existing research needs to be strengthened, and future research work should improve the quality of research and improve the prediction effect of the model by expanding the sample size.