PubMed 比 Web of Science 捕获到更多关于科学评论的细粒度书目数据：一项比较分析。

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

机构信息

Institute of Medical Technology, Peking University Health Science Center, Beijing, China.

National Institute of Health Data Science, Peking University, Beijing, China.

出版信息

BMJ Health Care Inform. 2024 Oct 11;31(1):e101017. doi: 10.1136/bmjhci-2024-101017.

DOI:10.1136/bmjhci-2024-101017

PMID:39395833

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11474939/

Abstract

BACKGROUND

Research commentaries have the potential for evidence appraisal in emphasising, correcting, shaping and disseminating scientific knowledge.

OBJECTIVES

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

摘要

背景

研究评论具有强调、纠正、塑造和传播科学知识的潜力。

目的

为了确定捕获评论信息的适当文献来源，本研究比较了 PubMed 和 Web of Science (WoS) 中的评论数据，以评估它们在证据评估中的适用性。

方法

以 COVID-19 为例，PubMed 中有超过 27k 篇 COVID-19 论文作为基线，我们从同一个数据集池中为两个数据库设计了一项比较分析，进行了公平且可靠的比较。我们为每个数据库构建了评论网络，用于网络结构分析，并从各个方面比较了评论材料和被评论论文的特征。

结果

对于网络比较，PubMed 具有更多的封闭反馈循环，与 WoS 的四个层次相比，它可以达到更深的六个层次的网络，使得通过论证挖掘进行证据评估更适合 PubMed。PubMed 在识别专门的评论方面表现出色，评论作者数量（平均值，3.59）和评论页数（平均值，1.86）明显低于 WoS（作者，4.31，95%置信区间差值= [0.66，0.79]，p<0.001；页，2.80，95%置信区间差值= [0.87，1.01]，p<0.001），这归因于 PubMed 的 CICO 评论识别算法。PubMed 中的被评论论文也显示出更高的引用量和更强的情绪，尤其是争议率明显升高（PubMed，24.54%；WoS，18.8%；基线，8.3%；均 p<0.0001）。此外，与 WoS 相比，两个来源的被评论论文都表现出更好的网络中心性指标。