Early identification and intervention of acute respiratory distress syndrome (ARDS) are particularly important. This study aimed to construct predictive models for ARDS following severe acute pancreatitis (SAP) by artificial neural networks and logistic regression. The artificial neural networks model was constructed using clinical data from 214 SAP patients. The patient cohort was randomly divided into a training set and a test set, with 149 patients allocated to the training set and 65 patients assigned to the test set. The artificial neural networks and logistic regression models were trained by the training set, and then the performance of both models was evaluated using the test set. The sensitivity, specificity, PPV, NPV, accuracy, and AUC value of artificial neural networks model were 68.0%, 87.5%, 77.3%, 81.4%, 80.0%, 0.853 ± 0.054 (95% CI: 0.749–0.958). The sensitivity, specificity, PPV, NPV, accuracy and AUC value of logistic regression model were 48.7%, 85.3%, 65.5%, 74.4%, 72.0%, 0.799 ± 0.045 (95% CI: 0.710–0.888). There were no significant differences between the artificial neural networks and logistic regression models in predictive performance. Bedside Index of Severity in Acute Pancreatitis score, procalcitonin, prothrombin time, and serum calcium were the most important predictive variables in the artificial neural networks model. The discrimination abilities of logistic regression and artificial neural networks models in predicting SAP-related ARDS were similar. It is advisable to choose the model according to the specific research purpose.
Objectives: The effect of a retracted publication can be significant, especially in high-impact journals. We aimed to identify the characteristics of retracted articles in three high-impact clinical journals, including The Journal of the American Medical Association (JAMA), Lancet, and The New England Journal of Medicine (NEJM).Methods: The Retraction Watch database was searched from inception to March 21, 2021. Data collected for each article included title, article type, reasons for retraction, journal, publisher, publication year, retraction year, first author, country of origin, and times a retracted article was cited. Data analysis was performed by GraphPad Prism version 8.00 (La Jolla, CA, United States), and SPSS Statistics 26.0. Results: A total of 77 articles were retracted, of which 20 were from JAMA, 31 from Lancet, and 26 from NEJM. Over one-half of papers were retracted within the last decade (n=44, 57.1%). Since 2005, the number of retracted articles had increased dramatically. The United States (n=29, 37.7%) was the country with the highest number of retracted publications in the three journals, followed by the United Kingdom (n=9, 11.7%) and China (n=5, 6.5%). The most common reasons for retractions were “Error” (n=20, 26.0%), “Fraud” (n=19, 24.7%) and “Reliability” (n=18, 23.4%) in these three journals. The media number of citations was 79 (range 0-2677). The media time to retraction was 651 (range 2-8474) days. The time was longer when an article was retracted because of fraud than that because of error. Conclusions: By analyzing the features of retracted articles in the three high-impact journals, we could identify the reason for retraction more comprehensively and benefit in improving the quality of biomedical literature.
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