Pharmaceutical payments to certified oncology specialists in Japan in 2016: a retrospective observational cross-sectional analysis.

OBJECTIVE: This study investigated payments made by pharmaceutical companies to oncology specialists in Japan, what the payments were for and whether the receipt of such payments contravened any conflict of interest (COI) regulations. DESIGN, SETTING AND PARTICIPANTS: Payment data to physicians, as reported by all pharmaceutical companies belonging to the Japan Pharmaceutical Manufacturers Association, were retrospectively extracted for 2016. Of the named individual recipients of payments, all certified oncologists were identified, using certification data from the Japanese Society of Medical Oncology (JSMO). The individual specialisations of each of the oncologists was also identified. OUTCOME: Payments to individual cancer specialists and what they were for were identified. Factors associated with receipt of higher value payments and payment flows to specialties were determined. Companies selling oncology drugs with annual sales of ≥5 billion yen (£33.9 million, €40.2 million and $46.0 million) (high revenue-generating drugs) were identified. RESULTS: In total, 59 companies made at least one payment to oncologists. Of the 1080 oncology specialists identified, 763 (70.6%) received at least one payment, while 317 received no payment. Of the 763, some 142 (13.1%) receiving at least 1 million yen (£6,800, €8,000 and $9200) accounted for 71.5% of the total. After adjustment of covariates, working for university hospitals and cancer hospitals and male gender were key factors associated with larger monetary payments. Payments preferentially targeted on cancer specialties using high revenue-generating drugs. The JSMO has its own COI policy for its members, but the policy did not mention any specific guidelines for certified oncology specialists. CONCLUSION: Financial relationships were identified and quantified between pharmaceutical companies and oncology specialists, but the extent and worth varied significantly. Given the frequency and amounts of money involved in such linkages, it would be beneficial for specific COI regulations to be developed and policed for oncologists.

Twitter use in scientific communication revealed by visualization of information spreading by influencers within half a year after the Fukushima Daiichi nuclear power plant accident.

Scientific communication through social media, particularly Twitter has been gaining importance in recent years. As such, it is critical to understand how information is transmitted and dispersed through outlets such as Twitter, particularly in emergency situations where there is an urgent need to relay scientific information. The purpose of this study is to examine how original tweets and retweets on Twitter were used to diffuse radiation related information after the Fukushima Daiichi nuclear power plant accident. Out of the Twitter database, we purchased all tweets (including replies) and retweets related to Fukushima Daiichi nuclear power plant accident and or radiation sent from March 2nd, 2011 to September 15th, 2011. This time frame represents the first six months after the East Japan earthquake, which occurred on March 11th, 2011. Using the obtained data, we examined the number of tweets and retweets and found that only a small number of Twitter users were the source of the original posts that were retweeted during the study period. We have termed these specific accounts as "influencers". We identified the top 100 influencers and classified the contents of their tweets into 3 groups by analyzing the document vectors of the text. Then, we examined the number of retweets for each of the 3 groups of influencers, and created a retweet network diagram to assess how the contents of their tweets were being spread. The keyword "radiation" was mentioned in over 24 million tweets and retweets during the study period. Retweets accounted for roughly half (49.7%) of this number, and the top 2% of Twitter accounts defined as "influencers" were the source of the original posts that accounted for 80.3% of the total retweets. The majority of the top 100 influencers had individual Twitter accounts bearing real names. While retweets were intensively diffused within a fixed population, especially within the same groups with similar document vectors, a group of influencers accounted for the majority of retweets one month after the disaster, and the share of each group did not change even after proven scientific information became more available.

Visualization of JOV abstracts.

ABSTRACT: Since the abstract can be found at the beginning of most scientific articles and is an essential part of the article, several attempts have been made to explore the rhetorical moves of abstracts in various research fields. These studies dealt only with accepted articles since they can be easily accessed. Although the findings of such works have some pedagogical implications for academic writing courses for young researchers who are relatively new to their fields, they do not contribute enough to the transparency of the peer review processes conducted in research fields. Increasing transparency requires considering rejected articles since they help to clarify the decision criteria in the peer review. Based on 591 abstracts of accepted or rejected articles submitted to Journal of Visualization (JOV), the present study aimed at exploring the differences between the accepted and rejected abstracts. The results show that there are significant differences in the structures of the abstracts. Since we also successfully develop a classification model for the decision using a machine-learning technique, the findings of this study have some implications for developing a semi-automatic reviewing system that can reduce the reviewer's burden and increase the review quality.

Layered Graph Drawing for Visualizing Evaluation Structures.

An evaluation structure is a hierarchical structure of human cognition extracted from interviews based on the evaluation grid method. An evaluation structure can be defined as a directed acyclic graph (DAG). The authors propose a layer-assignment method that is part of the Sugiyama framework, a popular method for drawing DAGs, to satisfy the requirements for drawing evaluation structures. Their evaluations demonstrate that the layered graph drawing produced by the proposed layer-assignment method is preferred by users and aids in the understanding of evaluation structures.

Minimizing the Number of Edges via Edge Concentration in Dense Layered Graphs.

Edge concentration in dense bipartite graphs is a technique for reducing the numbers of edges and edge crossings in graph drawings. The conventional method proposed by Newbery is designed to reduce the number of edge crossings; however, it does not always reduce the number of edges. Reducing the number of edges is also an important factor for improving the readability of graphs. However, no edge concentration method with the explicit purpose of minimizing the number of edges has previously been studied. In this study, we propose a novel, efficient heuristic method for minimizing the number of edges during edge concentration. We demonstrate the efficiency of the proposed method via a comparison using randomly generated graphs. We find that Newbery's method fails to reduce the number of edges when the number of vertices is large. By contrast, the proposed method achieves an average compression ratio of 47 to 82 percent for all generated graph groups. We also present a real-world application of the proposed method using a causality network of biological data.