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Mixed-membership models of scientific publications.

ABSTRACT: PNAS is one of world's most cited multidisciplinary scientific journals. The PNAS official classification structure of subjects is reflected in topic labels submitted by the authors of articles, largely related to traditionally established disciplines. These include broad field classifications into physical sciences, biological sciences, social sciences, and further subtopic classifications within the fields. Focusing on biological sciences, we explore an internal soft-classification structure of articles based only on semantic decompositions of abstracts and bibliographies and compare it with the formal discipline classifications. Our model assumes that there is a fixed number of internal categories, each characterized by multinomial distributions over words (in abstracts) and references (in bibliographies). Soft classification for each article is based on proportions of the article's content coming from each category. We discuss the appropriateness of the model for the PNAS database as well as other features of the data relevant to soft classification.

SUBMITTER: Erosheva E

PROVIDER: S-EPMC387299 | biostudies-literature | 2004 Apr

REPOSITORIES: biostudies-literature

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Mixed-membership models of scientific publications.

Erosheva Elena E Fienberg Stephen S Lafferty John J

Proceedings of the National Academy of Sciences of the United States of America 20040312

PNAS is one of world's most cited multidisciplinary scientific journals. The PNAS official classification structure of subjects is reflected in topic labels submitted by the authors of articles, largely related to traditionally established disciplines. These include broad field classifications into physical sciences, biological sciences, social sciences, and further subtopic classifications within the fields. Focusing on biological sciences, we explore an internal soft-classification structure o ...[more]

PMID: 15020766

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Project description:In addition to its primary regulatory role, the Office of Hematology and Oncology Products at the U.S. Food and Drug Administration (FDA) is engaged in many forms of scientific authorship. During the period of 2010 to 2018, FDA oncology staff contributed to 356 publications in the scientific literature. Here, we collaborated with analysts in the Office of Program Planning, Analysis, and Evaluation at the National Institute of General Medical Sciences, National Institutes of Health (NIH), to present a series of analyses aimed at quantifying the characteristics and potential impact of these contributions, as well as characterizing the areas of work addressed. We found that FDA oncology papers are enriched for high-impact publications and have about two times the number of citations as an average NIH-funded paper. Further impact of the publications was measured based on the presence of 65 publications that were cited by guidelines and 12 publications cited by publicly listed clinical trials. The results seen here are promising in determining the impact of FDA oncology publication work but prompt further investigation into longer-term impacts, such as the influence of this work on other regulatory activities at FDA. IMPLICATIONS FOR PRACTICE: This article describes the first comprehensive study of scientific publications produced by U.S. Food and Drug Administration (FDA) oncology staff. The analysis illustrates that staff are highly engaged in publishing in the scientific literature in addition to completing regulatory review work. Publications are generally in clinical medicine, consistent with the large number of medical oncologists working at the Office of Hematology and Oncology Products (OHOP). OHOP publications generally focus either on communicating important regulatory work (approval summaries) or highlighting regulatory science issues to encourage dialogue with the scientific community (commentaries, reviews, and expert working papers). The analysis also suggests that several FDA oncology publications may influence clinical guidelines, but further work is needed to evaluate impact.

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Mixed-membership models of scientific publications.

Publications

Mixed-membership models of scientific publications.

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