PubMed captures more fine-grained bibliographic data on scientific commentary than Web of Science: a comparative analysis.

Abstract

Research commentaries have the potential for evidence appraisal in emphasising, correcting, shaping and disseminating scientific knowledge. To identify the appropriate bibliographic source for capturing commentary information, this study compares comment data in PubMed and Web of Science (WoS) to assess their applicability in evidence appraisal. Using COVID-19 as a case study, with over 27 k COVID-19 papers in PubMed as a baseline, we designed a comparative analysis for commented-commenting relations in two databases from the same dataset pool, making a fair and reliable comparison. We constructed comment networks for each database for network structural analysis and compared the characteristics of commentary materials and commented papers from various facets. For network comparison, PubMed surpasses WoS with more closed feedback loops, reaching a deeper six-level network compared with WoS' four levels, making PubMed well-suited for evidence appraisal through argument mining. PubMed excels in identifying specialised comments, displaying significantly lower author count (mean, 3.59) and page count (mean, 1.86) than WoS (authors, 4.31, 95% CI of difference of two means = [0.66, 0.79], p<0.001; pages, 2.80, 95% CI of difference of two means = [0.87, 1.01], p<0.001), attributed to PubMed's CICO comment identification algorithm. Commented papers in PubMed also demonstrate higher citations and stronger sentiments, especially significantly elevated disputed rates (PubMed, 24.54%; WoS, 18.8%; baseline, 8.3%; all p<0.0001). Additionally, commented papers in both sources exhibit superior network centrality metrics compared with WoS-only counterparts. Considering the impact and controversy of commented works, the accuracy of comments and the depth of network interactions, PubMed potentially serves as a valuable resource in evidence appraisal and detection of controversial issues compared with WoS.