PubMed · 3205286
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1988. Copying & copyright.. https://pubmed.ncbi.nlm.nih.gov/3205286/
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BACKGROUND: The pharmaceutical industry has become a major source of funding for biomedical research. Our general observation is that pharmaceutical industry employees are appearing with increasing frequency as coauthors of clinical trial publications. OBJECTIVE: To characterize clinical trial funding, reporting, and sources; investigate author-industry affiliation; and describe clinical outcome trends over time. METHODS: We reviewed 500 randomly selected clinical trials published in 5 influential medical journals over a 20-year period (1981-2000). RESULTS: Of the 500 clinical trials reviewed, 181 (36%) involved pharmaceutical industry as an independent (n = 104) or joint (n = 77) sponsor and 180 (36%) involved a peer-review funding source; the balance (139; 28%) lacked any declared sponsorship. The percentage of industry-sponsored clinical trials increased to 62% during 1997-2000. The percentage of nonprofit sponsored clinical trials remained constant over time, while the percentage of those without funding declaration declined. Reported author affiliation with industry increased to 66% of clinical trials sponsored only by industry. An increase in the percentage of clinical trials with reported author-industry affiliation was observed for all journals. Regardless of funding source, the majority of clinical trials reported clinical outcomes that favored the study drug. CONCLUSIONS: Pharmaceutical industry-sponsored and mixed-funding clinical trials are common, and the relative incidence of published trials with these declared funding sources in the 5 journals reviewed has increased. Industry employees are appearing as coauthors of clinical trial publications with increasing frequency.
There has been a long history of research into the structure and evolution of mankind's scientific endeavor. However, recent progress in applying the tools of science to understand science itself has been unprecedented because only recently has there been access to high-volume and high-quality data sets of scientific output (e.g., publications, patents, grants) and computers and algorithms capable of handling this enormous stream of data. This article reviews major work on models that aim to capture and recreate the structure and dynamics of scientific evolution. We then introduce a general process model that simultaneously grows coauthor and paper citation networks. The statistical and dynamic properties of the networks generated by this model are validated against a 20-year data set of articles published in PNAS. Systematic deviations from a power law distribution of citations to papers are well fit by a model that incorporates a partitioning of authors and papers into topics, a bias for authors to cite recent papers, and a tendency for authors to cite papers cited by papers that they have read. In this TARL model (for topics, aging, and recursive linking), the number of topics is linearly related to the clustering coefficient of the simulated paper citation network.
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