Understanding the nature and scope of clinical research commentaries in PubMed

A comment-driven evidence appraisal approach to promoting research findings into practice when only uncertain evidence is available

BACKGROUND

Comments in PubMed are usually short papers for supporting or refuting claims, or discussing methods and findings in original articles. This study aims to explore whether they can be used as a quick and reliable evidence appraisal instrument for promoting research findings into practice, especially in emergency situations such as COVID-19 in which only missing, incomplete or uncertain evidence is available.

METHODS

Evidence-comment networks (ECNs) were constructed by linking COVID-19-related articles to the commentaries (letters, editorials or brief correspondence) they received. PubTator Central was used to extract entities with a high volume of comments from the titles and abstracts of the articles. Among them, six drugs were selected, and their evidence assertions were analysed by exploring the structural information in the ECNs as well as the sentiment of the comments (positive, negative, neutral). Recommendations in WHO guidelines were used as the gold standard control to validate the consistency, coverage and efficiency of comments in reshaping clinical knowledge claims.

RESULTS

The overall positive/negative sentiments of comments were aligned with recommendations for/against the corresponding treatments in the WHO guidelines. Comment topics covered all significant points of evidence appraisal and beyond. Furthermore, comments may indicate the uncertainty regarding drug use for clinical practice. Half of the critical comments emerged 4.25 months earlier on average than the guideline release.

CONCLUSIONS

Comments have the potential as a support tool for rapid evidence appraisal as they have a selection effect by appraising the benefits, limitations and other clinical practice issues of concern in existing evidence. We suggest as a future direction an appraisal framework based on the comment topics and sentiment orientations to leverage the potential of scientific commentaries supporting evidence appraisal and decision-making.

Retrospective and prospective approaches of coronavirus publications in the last half-century: a Latent Dirichlet allocation analysis

Purpose

The present article's primary purpose is the topic modeling of the global coronavirus publications in the last 50 years.

Design/methodology/approach

The present study is applied research that has been conducted using text mining. The statistical population is the coronavirus publications that have been collected from the Web of Science Core Collection (1970–2020). The main keywords were extracted from the Medical Subject Heading browser to design the search strategy. Latent Dirichlet allocation and Python programming language were applied to analyze the data and implement the text mining algorithms of topic modeling.

Findings

The findings indicated that the SARS, science, protein, MERS, veterinary, cell, human, RNA, medicine and virology are the most important keywords in the global coronavirus publications. Also, eight important topics were identified in the global coronavirus publications by implementing the topic modeling algorithm. The highest number of publications were respectively on the following topics: “structure and proteomics,” “Cell signaling and immune response,” “clinical presentation and detection,” “Gene sequence and genomics,” “Diagnosis tests,” “vaccine and immune response and outbreak,” “Epidemiology and Transmission” and “gastrointestinal tissue.”

Originality/value

The originality of this article can be considered in three ways. First, text mining and Latent Dirichlet allocation were applied to analyzing coronavirus literature for the first time. Second, coronavirus is mentioned as a hot topic of research. Finally, in addition to the retrospective approaches to 50 years of data collection and analysis, the results can be exploited with prospective approaches to strategic planning and macro-policymaking.