ObjectiveTo systematically review the efficacy and safety of Molnupiravir in the treatment of COVID-19. MethodsThe CNKI, VIP, WanFang Data, PubMed, Web of Science, Cochrane Library, and Epistemonikos COVID-19 L·OVE databases were electronically searched to collect randomized controlled trials (RCTs) related to Molnupiravir therapy for COVID-19 from inception to July, 2023. Two reviewers independently screened literature, extracted data and assessed the risk of bias of the included studies. Meta-analysis was then performed by using RevMan 5.4 software. ResultsA total of 9 RCTs involving 32 086 patients were included. The meta-analysis results revealed that no significant differences were observed in the 28-29 day hospitalization rate, the 28-29 day mortality rate, 14-15 day PCR test conversion rate, or adverse event incidence between the two groups. However, there was a significant increase in adverse events related to four types of systemic organ diseases in the Monolaurin group. Conclusion Current evidence shows that the safety profile of Monolaurin and its potential benefits for COVID-19 patients with a high risk of progressing to severe illness is unclear. Due to the limited quality and quantity of the included studies, more high quality studies are needed to verify the above conclusion.
ObjectiveTo verify the influence of different variable selection methods on the performance of clinical prediction models. MethodsThree sample sets were extracted from the MIMIC database (acute myocardial infarction group, sepsis group, and cerebral hemorrhage group) using the direct entry of COX regression, step by step forward, step by step backward, LASSO, and ridge regression, based on random forest. These existing six methods of variable importance algorithm, and the optimal variable set of different selected methods were used to construct the model. Through the C index, the area under the ROC curve (AUC value) and the calibration curve, and the results within and between groups were compared. ResultsThe variables and numbers selected by the six variable selection methods were different, however, whether it was within or between groups did not reflect which method had the advantage of significantly improving the performance of the model. ConclusionsPrior to using the variable selection method to establish a clinical prediction model, we should first clarify the research purpose and determine the type of data. Combining medical knowledge to select a method that can meet the data type and simultaneously achieve the research purpose.
ObjectiveTo investigate the clinical research development of dementia in the UK Biobank database in SCIE and PubMed. MethodsThe literatures of dementia in the UK Biobank database published in SCIE and PubMed from January 1, 2018 to November 30, 2022 were searched, and the number of articles, publishing institutions, journals, citations, authors and keywords were statistically analyzed. ResultsA total of 279 papers were included, and the number of papers presented an annual growth trend. The United Kingdom has the largest number of publications, the United States journals have the greatest influence, and China has the third largest number of publications. Springer Nature from Germany published the most papers, with the largest number of 47 papers. Among the authors, Yu JT from China published the most, with 11 articles, and the most major keyword in the research content is Alzheimer. ConclusionThe literatures of dementia in the UK Biobank-related field included in SCIE and PubMed databases show an increasing trend year by year, mainly in English, and the core author group has not yet formed. The papers published by Chinese scholars are concentrated in 2020-2022, and there are few transnational cooperative papers.