Lantox—The Chinese Botulinum Toxin Drug—Complete English Bibliography and Comprehensive Formalised Literature Review

In 1997, lanbotulinumtoxinA (LAN) was introduced in China. It is now available in Asia, Latin America and Eastern Europe under various brand names including Hengli®, Lantox®, Prosigne®, Lanzox®, Redux®, Liftox®, HBTX-A and CBTX-A. The literature on LAN is mostly published in Chinese language, restricting its international accessibility. We, therefore, wanted to generate a complete English bibliography of all LAN publications and then use it for a comprehensive formalised literature review. Altogether, 379 LAN publications (322 in Chinese and 57 in English) were retrieved from PubMed and Science and Technology Paper Citation Database. Indications covered are motor (257), glandular (16), pain (32) and aesthetics (48). Topics are neurological (250), aesthetic (48), paediatric (38), ophthalmological (18), urological (9), methodological (6), gastroenterological (5), ear, nose and throat (4) and surgical (1). Seventy-one publications are randomised controlled trials, forty-one publications are interventional studies and observational studies, fifteen publications are case studies, eighteen publications are reviews, and two publications are guidelines. LAN publications cover all relevant topics of BT therapy throughout a period of more than 20 years. This constitutes a publication basis resembling those of other BT drugs. None of the LAN publications presents data contradictory to those generated with other BT type-A drugs. LAN seems to have a similar efficacy and safety features when compared to onabotulinumtoxinA using a 1:1 LAN– onabotulinumtoxinA conversion ratio. Large controlled multicentre studies will become necessary for LAN’s registrations in Europe and North America.

Linking science to technology: the “patent paper citation” and the rise of patentometrics in the 1980s

PurposeIn this article, the ideas and methods behind the “patent-paper citation” are scrutinised by following the intellectual and technical development of approaches and ideas in early work on patentometrics. The aim is to study how references from patents to papers came to play a crucial role in establishing a link between science and technology.Design/methodology/approachThe study comprises a conceptual history of the “patent paper citation” and its emergence as an important indicator of science and technology interaction. By tracing key references in the field, it analyses the overarching frameworks and ideas, the conceptual “hinterland”, in which the approach of studying patent references emerged.FindingsThe analysis explains how interest in patents – not only as legal and economic artefacts but also as scientific documents – became evident in the 1980s. The focus on patent citations was sparked by a need for relevant and objective indicators and by the greater availability of databases and methods. Yet, the development of patentometrics also relied on earlier research, and established theories, on the relation between science and technology.Originality/valueThis is the first attempt at situating patentometrics in a larger societal and scientific context. The paper offers a reflexive and nuanced analysis of the “patent-paper citation” as a theoretical and historical construct, and it calls for a broader and contextualised understanding of patent references, including their social, legal and rhetorical function.

Keywords-Driven and Weight-aware Paper Recommendation via Paper Correlation Pattern Mining

Currently, readers often prefer to search for their interested papers based on a set of typed query keywords. As the keywords of a paper is often limited, paper recommender systems often need to recommend a set of papers which collectively satisfy the readers’ keyword query. However, the topics of recommended papers are probably not correlated with each other, which fail to meet the readers’ requirements on in-depth and continuous academic research. Furthermore, although existing paper citation graphs can model the papers’ correlations, they often face the data sparse problem which blocks accurate paper recommendations. To address these issues, we propose a keywords-driven and weight-aware paper recommendation approach, named LP-PRk+w (link prediction-paper recommendation), based on a weighted paper correlation graph. Concretely, we firstly optimize the existing paper citation graph modes by introducing a weighted similarity, after which we obtain a weighted paper correlation graph. Then we recommend a set of correlated papers based on the weighted paper correlation graph and the query keywords from readers. At last, we conduct large-scale experiments on a real-world Hep-Th dataset. Experimental results demonstrate that our proposal can improve the paper recommendation performances considerably, compared to other related solutions.

Addressing Nursing Scholarship: A Framework for Currency and Number of References

Background Timeliness and number of references in written work is often a topic of controversy. Decisions about choice of references become complex when there is little recent published information or a great deal of important historical work on a topic. Purpose The study aim was to develop a framework to guide authors to determine the number and currency of references to support their writing. Methods This study used a descriptive design with three steps: review of journal author information for guidance about reference currency (n = 247); correspondence with journal editors (n = 27); and a survey of nurse educators (n = 44) regarding currency and number of references in written assignments. Results Findings affirmed that recent literature is vital for nursing scholarship. Numerical guidelines offered were not based on identifiable consensus or rationale. Historical perspectives published over 5 or 10 years earlier are valued, even sometimes required. For a clinical paper, citation of the most current literature is viewed by editors and educators as essential, and may suffice. Conclusion Based on the findings of this study and our search of the literature, we developed three decision making algorithms for searching the literature and selecting references by currency and number.

Towards a More Realistic Citation Model: The Key Role of Research Team Sizes

We propose a new citation model which builds on the existing models that explicitly or implicitly include “direct” and “indirect” (learning about a cited paper’s existence from references in another paper) citation mechanisms. Our model departs from the usual, unrealistic assumption of uniform probability of direct citation, in which initial differences in citation arise purely randomly. Instead, we demonstrate that a two-mechanism model in which the probability of direct citation is proportional to the number of authors on a paper (team size) is able to reproduce the empirical citation distributions of articles published in the field of astronomy remarkably well, and at different points in time. Interpretation of our model is that the intrinsic citation capacity, and hence the initial visibility of a paper, will be enhanced when more people are intimately familiar with some work, favoring papers from larger teams. While the intrinsic citation capacity cannot depend only on the team size, our model demonstrates that it must be to some degree correlated with it, and distributed in a similar way, i.e., having a power-law tail. Consequently, our team-size model qualitatively explains the existence of a correlation between the number of citations and the number of authors on a paper.

Keywords-Driven and Popularity-Aware Paper Recommendation Based on Undirected Paper Citation Graph

Nowadays, scholar recommender systems often recommend academic papers based on users’ personalized retrieval demands. Typically, a recommender system analyzes the keywords typed by a user and then returns his or her preferred papers, in an efficient and economic manner. In practice, one paper often contains partial keywords that a user is interested in. Therefore, the recommender system needs to return the user a set of papers that collectively covers all the queried keywords. However, existing recommender systems only use the exact keyword matching technique for recommendation decisions, while neglecting the correlation relationships among different papers. As a consequence, it may output a set of papers from multiple disciplines that are different from the user’s real research field. In view of this shortcoming, we propose a keyword-driven and popularity-aware paper recommendation approach based on an undirected paper citation graph, named PRkeyword+pop. At last, we conduct large-scale experiments on the real-life Hep-Th dataset to further demonstrate the usefulness and feasibility of PRkeyword+pop. Experimental results prove the advantages of PRkeyword+pop in searching for a set of satisfactory papers compared with other competitive approaches.

SAP Algorithm for Citation Analysis: An improvement to Tree of Science

Tree of Science (ToS) is a web-based tool which uses the network structure of paper citation to identify relevant literature. ToS shows the information in the form of a tree, where the articles located in the roots are the classics, in the trunk are the structural publications, and leaves are the most current papers. It has been found that some results in the leaves can be separated from the tree. Therefore, an algorithm (SAP) is proposed, in order to improve results in the leaves. Two improvements are presented: articles located in the leaves are from the last five years, and they are connected to root and trunk articles through their citations. This improvement facilitates construction of current literature for researchers.