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The use of social media is one of the new technological options that has been recommended as a potential new strategy for delivering high-quality, high-value cancer prevention and management services. Despite the increasing use of social media, little research has been done on the use of social media in brain tumors. Therefore, this systematic review aims to provide a comprehensive review of the use of social media in brain tumor research.

A systematic search was performed in PubMed, Scopus and Web of Science from inception to August 1, 2022. English full-text articles evaluating social media use, benefit or content in brain tumor were considered.

Sixteen documents satisfied the inclusion criteria and were included in the final analysis. Most of the included studies (n = 11/16) were conducted and published by researchers in the USA. In terms of social media platform, most studies focused on Twitter (8/16, 50%) and YouTube (8/16, 50%), followed by Facebook (6/16, 37.5%) and Instagram (4/16, 25%). Most studies (n = 7/12) analyzed the content of brain tumor information provided on social media, followed by patients’ use of social media (n = 3/12) and the quality of information on social media (n = 3/12). The other three articles also examined patient recruitment, crowdfunding and caregiver use of social media.

By identifying the use, benefits and content of social media platforms in different settings, patients, clinicians and policymakers can better benefit from harnessing the power of social media in different ways, leading to improved health-care services.

To the authors knowledge, this is the first study to systematically examine social media use, benefits and content status in brain tumors.

No comprehensive statistical assessment of publication bias has been conducted in remdesivir-based intervention research for COVID-19 patients. This study aims to examine all meta-analyses of the efficacy of remdesivir interventions in COVID-19 patients and perform a statistical assessment of publication bias.

This is an analytic study conducted to assess the impact of publication bias on the results of meta-analyses of remdesivir-based interventions in patients infected with COVID-19. All English full-text meta-analyses published in peer-reviewed journals in 2019–2021 were included. A computerized search of PubMed and Web of Science electronic databases was performed on December 24, 2021. The trim-and-fill method calculated the number of missing studies and the adjusted cumulative effect sizes.

The final analysis comprised 21 studies with 88 outcomes. The investigation revealed missing studies in 46 outcomes (52%). Seventy-six missing studies were replaced in the outcomes using the trim-and-fill procedure. The adjusted recalculated effect sizes of the 27 outcomes increased by an average of 0.04. In comparison, the adjusted effect size of 18 outcomes fell by an average of 0.036. Only 14 out of 46 outcomes with publication bias were subjected to a gray literature search (30%). To discover related research, no gray literature search was conducted in most outcomes with publication bias (n = 32; 70%). In conclusion, the reported effect estimates regarding the effect of remdesivir in COVID-19 patients are only slightly affected by publication bias and can be considered authentic. Health-care decision-makers in COVID-19 should consider current research results when making clinical decisions.

Most health decisions are based on the effect sizes revealed in meta-analyses. When deciding on remdesivir-based treatment for COVID-19 patients, therefore, the outcomes of this investigation may be of paramount importance to health policymakers, leading to better treatment strategies.

According to the results, no major publication bias and missing studies were detected on average. Therefore, the calculated effect sizes of remdesivir-based interventions on meta-analyses can be used as authentic and unbiased benchmarks by health-care decision-makers in treating patients with COVID-19.

This is the first study to examine the effect of publication bias and gray literature searches on the results of meta-analyses of treatment with COVID-19 (remdesivir).

The purpose of this study is to assess the e-readiness status of libraries in the Shahid Beheshti University of Medical Sciences in terms of four dimensions – human resources, electronical infrastructure, network services and programs and enhancers of the networked world.

The study population consists of 11 libraries of the Shahid Beheshti University of Medical Sciences, including the central library and 10 faculty libraries. The data collection instrument is a questionnaire prepared by the researchers that has been designed on the basis of the “e-readiness assessment of Iranian academic libraries model”. Depending on the respondents there are three parts to the questionnaire: questionnaire for managers, staff and information and communication technology (ICT) officials. Their reliability and validity have been proved.

The libraries of Shahid Beheshti University of Medical Sciences had an average to high status in terms of “human resources” with a score of 2.32, “electronic infrastructure” with a score of 2.48, “network services and programs” with a score of 2.09 and “networked world enhancers” with a score of 2.37 out of 4. In total, these libraries had an average to high status in terms of e-readiness, with a score of 2.29.

The findings of this study can help the library administrators and officials of Shahid Beheshti University of Medical Sciences to plan improvements to the situation of ICT.

This study aims to investigate the characteristics, as well as the purpose and posts of the COVID-19 Facebook groups.

A systematic search for COVID-19 Facebook groups was conducted on June 1, 2020. Characteristics of the groups were examined using descriptive statistics. Mann-Whitney test was used to study the differences between groups. The study of the most popular groups’ posts was also carried out using the content analysis method.

The groups had a combined membership of 2,729,061 users. A total of 147,885 posts were received. There were about approximately 60% public groups. A high percentage of the groups (86.5%) had descriptions. The results showed a significant relationship between the groups’ description status and the number of members (p-value = 0.016). The majority of COVID-19 Facebook groups (56%) were created to meet their members’ information needs. The highest number of studied posts were related to vaccination (35.2%), followed by curfew rules (19.6%) and symptoms (10.6%).

Translating these insights into policies and practices will put policymakers and health-care providers in a stronger position to make better use of Facebook groups to support and enhance public knowledge about COVID-19.

No study has investigated the effects of different parameters on publication bias in meta-analyses using a machine learning approach. Therefore, this study aims to evaluate the impact of various factors on publication bias in meta-analyses.

An electronic questionnaire was created according to some factors extracted from the Cochrane Handbook and AMSTAR-2 tool to identify factors affecting publication bias. Twelve experts were consulted to determine their opinion on the importance of each factor. Each component was evaluated based on its content validity ratio (CVR). In total, 616 meta-analyses comprising 1893 outcomes from PubMed that assessed the presence of publication bias in their reported outcomes were randomly selected to extract their data. The multilayer perceptron (MLP) technique was used in IBM SPSS Modeler 18.0 to construct a prediction model. 70, 15 and 15% of the data were used for the model's training, testing and validation partitions.

There was a publication bias in 968 (51.14%) outcomes. The established model had an accuracy rate of 86.1%, and all pre-selected nine variables were included in the model. The results showed that the number of databases searched was the most important predictive variable (0.26), followed by the number of searches in the grey literature (0.24), search in Medline (0.17) and advanced search with numerous operators (0.13).

The results of this study can help clinical researchers minimize publication bias in their studies, leading to improved evidence-based medicine.

To the best of the author’s knowledge, this is the first study to model publication bias using machine learning.

Predicting the final status of an ongoing process or a subsequent activity in a process is an important aspect of process management. Semi-structured business processes cannot be predicted by precise and mathematical methods. Therefore, artificial intelligence is one of the successful methods. This study aims to propose a method that is a combination of deep learning methods, in particular, the recurrent neural network and Markov chain.

The proposed method applies the BestFirst algorithm for the search section and the Cfssubseteval algorithm for the feature comparison section. This study focuses on the prediction systems of social insurance and tries to present a method that is less costly in providing real-world results based on the past history of an event.

The proposed method is simulated with real data obtained from Iranian Social Security Organization, and the results demonstrate that using the proposed method increases the memory utilization slightly more than the Markov method; however, the CPU usage time has dramatically decreased in comparison with the Markov method and the recurrent neural network and has, therefore, significantly increased the accuracy and efficiency.

This research tries to provide an approach capable of producing the findings closer to the real world with fewer time and processing overheads, given the previous records of an event and the prediction systems of social insurance.