Search

The article discusses the application and interpretability of predictive statistical data analysis methods in medicine. It highlights the importance of understanding medical phenomena and intervening to change outcomes, rather than solely focusing on prediction. The article acknowledges the potential of artificial intelligence (AI) methods in medicine, but also raises concerns about the lack of transparency and interpretability in predictive algorithms. It explores various methods, such as explainable AI (XAI) and visualizations, that aim to address these challenges. The article also emphasizes the need for combining different analysis methods, such as regression models and machine learning, to obtain reliable and interpretable results. Additionally, it discusses the role of meta-analysis in evidence-based medicine and the challenges of synthesizing results from predictive models. Overall, the article provides insights into the complexity and potential of predictive statistical data analysis methods in medicine. [Extracted from the article]

The lack of consistent presentation of results in published studies on the association between a quantitative explanatory variable and a quantitative dependent variable has been a long-term issue in evaluating the reported findings. Studies are analyzed and reported in a variety of ways. The main purpose of this review is to illustrate the procedures in summarizing and synthesizing research results from multivariate models with a quantitative outcome variable. The review summarizes the application of the standardized regression coefficient as an effect size index in the context of meta-analysis and describe how it can be estimated and converted from data presented in original research articles. An example of synthesis is provided using research articles on the association between childhood body mass index and carotid intima-media thickness in adult life. Finally, the paper shares practical recommendations for meta-analysts wanting to use the standardized regression coefficient in pooling findings. [ABSTRACT FROM AUTHOR]

Featured Application: In this work, Process Mining techniques are used with a curricular analytics approach, to model the educational trajectories of engineering students during their first courses. Curricular analytics is the area of learning analytics that looks for insights and evidence on the relationship between curricular elements and the degree of achievement of curricular outcomes. For higher education institutions, curricular analytics can be useful for identifying the strengths and weaknesses of the curricula and for justifying changes in learning pathways for students. This work presents the study of curricular trajectories as processes (i.e., sequence of events) using process mining techniques. Specifically, the Backpack Process Model (BPPM) is defined as a novel model to unveil student trajectories, not by the courses that they take, but according to the courses that they have failed and have yet to pass. The usefulness of the proposed model is validated through the analysis of the curricular trajectories of N = 4466 engineering students considering the first courses in their program. We found differences between backpack trajectories that resulted in retention or in dropout; specific courses in the backpack and a larger initial backpack sizes were associated with a higher proportion of dropout. BPPM can contribute to understanding how students handle failed courses they must retake, providing information that could contribute to designing and implementing timely interventions in higher education institutions. [ABSTRACT FROM AUTHOR]

Early prognosis of severe disease and preventive actions hang around as the mainstay in managing the novel SARS-COV-2 outbreak due to the lack of robust therapeutic strategies. Krebs von den Lungen-6 (KL-6 or KL-6/MUC1) is a relatively new discovered transmembrane mucoprotein that was shown to be a good predictor of disease severity in interstitial lung diseases (ILD). We aimed to systematically research the literature in order to assess the relationship between the KL-6 biomarker and prognosis of SARS-CoV-2 infection. A literature search was performed in PubMed, Embase, and Cochrane library databases from inception to 8 March 2021. After eligibility assessment, eight studies were finally included in the present systematic review. All included studies are observational and single-center. The data gathered suggests the importance of prognostic implications of KL-6 in COVID-19 as patients with a more severe disease had significantly higher levels of KL-6 at admission. Moreover, the KL-6 biomarker was associated with COVID-19 severity, lung lesion areas on computed tomography, pulmonary fibrosis, and coagulation disorders. The association with mortality is unclear and needs further research. More extensive trials are required to prove that facile, inexpensive, and good predictors of severe outcomes, such as KL-6, could be safely integrated into the clinical decision-making in patients with COVID-19. [ABSTRACT FROM AUTHOR]

In marketing, there is a notable lack of models, which leads to the loss of potential customers since there is no feedback for improvement in the marketing strategy. In the face of the Internet revolution, this study used digital channels and interaction to achieve potential customer retention through a Funnel; this allowed a potential client to go through the acquisition, conversion, and retention stages. In addition, activities were developed that allow the potential user to become a retained client. Thus, considering the stages of a marketing strategy, a non-probabilistic sample was used, composed of a pre-test period with 1088 users and a post-test period with 1450 users. Overall, there was a significant improvement in the time a client remained observing the website per session, with a 20.6% efficiency; furthermore, there was a 36.25% efficiency increase of the user conversion process per session in the digital channel. Likewise, a 46.43% efficiency increase was measured in visits to the digital channel, which confirms that the proposed marketing strategy significantly improves previous marketing strategies. Therefore, it is concluded that the proposal based on the elements of insight aligned with the measurement proposed by Google Analytics Key Performance Indicators, which was used to test and observe the improvements to continue the feedback, taking advantage of the characteristic of the digital channel with its two-way participation. [ABSTRACT FROM AUTHOR]

Featured Application: In this work, an applicable instrument is proposed to quickly evaluate the quality of the statistical reporting and data presentation in research papers. Background: Data analysis methods have become an essential part of empirical research papers, especially in health sciences and medical research. It has previously been reported that a noteworthy percentage of articles have flaws in their statistical reporting. Reporting problems have been a long-term issue, and despite continued efforts to improve the situation, improvements have been far from satisfactory. One explanation is an inadequate assessment of statistical reporting during peer review. This communication proposes a short instrument to assess the quality of data analysis reporting in manuscripts and published papers. Method: A checklist-type instrument was developed by selecting and refining items from previous reports about the quality of statistical reporting in medical journals and from published guidelines for reporting and data presentation. Items were pretested and modified during pilot studies. A total of 160 original medical research articles that were published in 4 journals were evaluated to test the instrument. Interrater and intrarater agreements were examined by comparing quality scores assigned to 40 articles published in a psychiatric journal. Results: The data analysis reporting test consists of nine questions that assess the quality of health research from a reader's perspective. The composed scale has a total score ranging from 0 to 10 and discriminated between journals and study designs. A high score suggested that an article had a good presentation of findings in tables and figures and that the description of analysis methods was helpful to readers. Interrater and intrarater agreements were high. Conclusion: An applicable checklist for quickly testing the statistical reporting quality of manuscripts and published research papers was developed. This instrument aims to improve the quality of empirical research in scientific fields where statistical methods play an important role. [ABSTRACT FROM AUTHOR]

Social media use among students has infiltrated into dental education and offers benefits but may also cause problems. The aim of this study was to explore and compare current social media usage among dental undergraduate students from two countries—Malaysia and Finland. A self-administered structured online questionnaire was used. WhatsApp, YouTube, Instagram, Facebook and Snapchat were the services that were most familiar to the respondents from both countries. There were differences between the students from the two countries among the most preferred platforms. The most frequently used applications were WhatsApp (91.1% of students in Malaysia and 96.1% in Finland used it very frequently) and Instagram (74.3% of students in Malaysia and 70.0% in Finland used it very frequently). Students in Malaysia spent significantly more hours per week using the platforms as study tools than students in Finland. Over 80% of the Finnish dental students reported that lack of knowledge was not an issue in social media usage, while 85% of Malaysian students felt that lack of knowledge prevented them from using social media platforms frequently. The findings offer evidence that dental students used social media extensively. [ABSTRACT FROM AUTHOR]

Proper peer review and quality of published articles are often regarded as signs of reliable scientific journals. The aim of this study was to compare whether the quality of statistical reporting and data presentation differs among articles published in 'predatory dental journals' and in other dental journals. We evaluated 50 articles published in 'predatory open access (OA) journals' and 100 clinical trials published in legitimate dental journals between 2019 and 2020. The quality of statistical reporting and data presentation of each paper was assessed on a scale from 0 (poor) to 10 (high). The mean (SD) quality score of the statistical reporting and data presentation was 2.5 (1.4) for the predatory OA journals, 4.8 (1.8) for the legitimate OA journals, and 5.6 (1.8) for the more visible dental journals. The mean values differed significantly (p < 0.001). The quality of statistical reporting of clinical studies published in predatory journals was found to be lower than in open access and highly cited journals. This difference in quality is a wake-up call to consume study results critically. Poor statistical reporting indicates wider general lower quality in publications where the authors and journals are less likely to be critiqued by peer review. [ABSTRACT FROM AUTHOR]

Although the sizes of business firms have been a subject of intensive research, the definition of a "size" of a firm remains unclear. In this study, we empirically characterize in detail the scaling relations between size measures of business firms, analyzing them based on allometric scaling. Using a large dataset of Japanese firms that tracked approximately one million firms annually for two decades (1994–2015), we examined up to the trivariate relations between corporate size measures: annual sales, capital stock, total assets, and numbers of employees and trading partners. The data were examined using a multivariate generalization of a previously proposed method for analyzing bivariate scalings. We found that relations between measures other than the capital stock are marked by allometric scaling relations. Power–law exponents for scalings and distributions of multiple firm size measures were mostly robust throughout the years but had fluctuations that appeared to correlate with national economic conditions. We established theoretical relations between the exponents. We expect these results to allow direct estimation of the effects of using alternative size measures of business firms in regression analyses, to facilitate the modeling of firms, and to enhance the current theoretical understanding of complex systems. [ABSTRACT FROM AUTHOR]

Background: A fundamental skill in education includes the ability to search for, evaluate, and synthesize information, and this cannot be underestimated in dental education. The aim of this study was to assess how dental students from Malaysia and Finland acquire scientific information and to compare their information retrieval skills. Methods: Fourth and fifth-year dental students from Malaysia and Finland were invited to participate. A self-administered structured questionnaire including items about the use of information sources, subjective assessment of literature retrieval skills and knowledge was used. Results: A total of 226 dental students participated in the survey: 131 from Malaysia and 95 from Finland. In both countries, the highest interest for data retrieval among students was found in the oral surgery specialty. The three most used sources of information among Malaysian students were personal lecture notes, dental textbooks, and colleagues; while Finnish students used colleagues, lecture notes, and current clinical guidelines. Students' knowledge of evidence-based practice was inadequate in both student groups. Though the majority of participants reported that they had good or passable skills in literature retrieval, more students from Finland judged themselves to have at least good skills compared to those from Malaysia. Conclusion: Dental education in both countries includes information retrieval studies and mandatory research projects. However, students did not often use those sources that are considered essential in evidence-based dentistry. Universities should further develop educational and training interventions that guide students to use knowledge resources more effectively for critically appraising scientific evidence. [ABSTRACT FROM AUTHOR]