Notes

Aseptic Necrosis regarding Femoral Go * Clinical Study.
To what extent do the vocabularies of mathematics, computing, astronomy, physics, chemistry, biology, psychology, sociology, economics, political science, philosophy, and linguistics overlap? To explore this question, samples of the anglophone vocabularies of these subjects were created using the Oxford English Dictionary (Benjafield in Scientometrics 1181051-1064, 2019. 10.1007/s11192-019-03021-2). The first part of this study compared the vocabularies of the five empirical members of Comte's hierarchy of the sciences (HoS) plus psychology (i.e., astronomy, physics, chemistry, biology, psychology, sociology). The results were generally consistent with the existence of an empirical HoS. For example, each subject shared its vocabulary the most with another subject adjacent to it in the hierarchy (i.e., astronomy with physics, physics with chemistry, biology with chemistry, psychology with biology, sociology with psychology). The second part of this study examined patterns of sharing between mathematics, computing, economics, political science, philosophy, linguistics and the six members of the empirical HoS. Among the most interesting results was the high degree of vocabulary sharing between mathematics, philosophy, and linguistics. SB525334 Indeed, it turns out that all subjects share their vocabularies with all other subjects, to varying degrees. It was suggested that, in addition to comparing subjects in terms of a linear HoS, similarities between subjects should be examined independently of their position on the HoS.The COVID-19 pandemic has been characterized by an unprecedented amount of published scientific articles. The aim of this study is to assess the type of articles published during the first 3 months of the COVID-19 pandemic and to compare them with articles published during 2009 H1N1 swine influenza pandemic. Two operators independently extracted and assessed all articles on COVID-19 and on H1N1 swine influenza that had an abstract and were indexed in PubMed during the first 3 months of these pandemics. Of the 2482 articles retrieved on COVID-19, 1165 were included. Over half of them were secondary articles (590, 50.6%). Common primary articles were human medical research (340, 59.1%), in silico studies (182, 31.7%) and in vitro studies (26, 4.5%). Of the human medical research, the vast majority were observational studies and cases series, followed by single case reports and one randomized controlled trial. Secondary articles were mainly reviews, viewpoints and editorials (373, 63.2%). Limitations were reported in 42 out of 1165 abstracts (3.6%), with 10 abstracts reporting actual methodological limitations. In a similar timeframe, there were 223 articles published on the H1N1 pandemic in 2009. During the COVID-19 pandemic there was a higher prevalence of reviews and guidance articles and a lower prevalence of in vitro and animal research studies compared with the H1N1 pandemic. In conclusions, compared to the H1N1 pandemic, the majority of early publications on COVID-19 does not provide new information, possibly diluting the original data published on this disease and consequently slowing down the development of a valid knowledge base on this disease. Also, only a negligible number of published articles reports limitations in the abstracts, hindering a rapid interpretation of their shortcomings. Researchers, peer reviewers, and editors should take action to flatten the curve of secondary articles.We study whether humans or machine learning (ML) classification models are better at classifying scientific research abstracts according to a fixed set of discipline groups. We recruit both undergraduate and postgraduate assistants for this task in separate stages, and compare their performance against the support vectors machine ML algorithm at classifying European Research Council Starting Grant project abstracts to their actual evaluation panels, which are organised by discipline groups. On average, ML is more accurate than human classifiers, across a variety of training and test datasets, and across evaluation panels. ML classifiers trained on different training sets are also more reliable than human classifiers, meaning that different ML classifiers are more consistent in assigning the same classifications to any given abstract, compared to different human classifiers. While the top five percentile of human classifiers can outperform ML in limited cases, selection and training of such classifiers is likely costly and difficult compared to training ML models. Our results suggest ML models are a cost effective and highly accurate method for addressing problems in comparative bibliometric analysis, such as harmonising the discipline classifications of research from different funding agencies or countries.The recent 'outburst' of COVID-19 spurred efforts to model and forecast its diffusion patterns, either in terms of infections, people in need of medical assistance (ICU occupation) or casualties. Forecasting patterns and their implied end states remains cumbersome when few (stochastic) data points are available during the early stage of diffusion processes. Extrapolations based on compounded growth rates do not account for inflection points nor end-states. In order to remedy this situation, we advance a set of heuristics which combine forecasting and scenario thinking. Inspired by scenario thinking we allow for a broad range of end states (and their implied growth dynamics, parameters) which are consecutively being assessed in terms of how well they coincide with actual observations. When applying this approach to the diffusion of COVID-19, it becomes clear that combining potential end states with unfolding trajectories provides a better-informed decision space as short term predictions are accurate, while a portfolio of different end states informs the long view. The creation of such a decision space requires temporal distance. Only to the extent that one refrains from incorporating more recent data, more plausible end states become visible. Such dynamic approach also allows one to assess the potential effects of mitigating measures. As such, our contribution implies a plea for dynamically blending forecasting algorithms and scenario-oriented thinking, rather than conceiving them as substitutes or complements.
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