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

IF 4.1 Q1 HEALTH CARE SCIENCES & SERVICES BMJ Health & Care Informatics Pub Date : 2024-10-11 DOI:10.1136/bmjhci-2024-101017
Shuang Wang, Kai Zhang, Jian Du
{"title":"PubMed captures more fine-grained bibliographic data on scientific commentary than Web of Science: a comparative analysis.","authors":"Shuang Wang, Kai Zhang, Jian Du","doi":"10.1136/bmjhci-2024-101017","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Research commentaries have the potential for evidence appraisal in emphasising, correcting, shaping and disseminating scientific knowledge.</p><p><strong>Objectives: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"31 1","pages":""},"PeriodicalIF":4.1000,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11474939/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMJ Health & Care Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1136/bmjhci-2024-101017","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0

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.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
与 "科学网"(Web of Science)相比,PubMed 获取的科学评论书目数据更精细:对比分析。
背景:研究评论在强调、纠正、塑造和传播科学知识方面具有证据评估的潜力:为了确定获取评论信息的合适文献来源,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:以COVID-19为案例,以PubMed中超过27 k篇的COVID-19论文为基线,我们设计了一项比较分析,从同一个数据集库中对两个数据库中的评论-评论关系进行了公平可靠的比较。我们分别构建了两个数据库的评论网络进行网络结构分析,并从不同侧面比较了评论材料和被评论论文的特点:在网络比较方面,PubMed 的反馈闭环比 WoS 更多,达到了更深的六级网络,而 WoS 只有四级,因此 PubMed 非常适合通过论据挖掘进行证据评估。PubMed 在识别专业评论方面表现出色,其作者数(平均值,3.59)和页数(平均值,1.86)均显著低于 WoS(作者数,4.31,两个平均值之差的 95% CI = [0.66, 0.79],pConclusion):考虑到评论作品的影响力和争议性、评论的准确性以及网络互动的深度,与 WoS 相比,PubMed 有可能成为证据评估和争议问题检测方面的宝贵资源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
6.10
自引率
4.90%
发文量
40
审稿时长
18 weeks
期刊最新文献
Scaling equitable artificial intelligence in healthcare with machine learning operations. Understanding prescribing errors for system optimisation: the technology-related error mechanism classification. Detection of hypertension from pharyngeal images using deep learning algorithm in primary care settings in Japan. PubMed captures more fine-grained bibliographic data on scientific commentary than Web of Science: a comparative analysis. Method to apply temporal graph analysis on electronic patient record data to explore healthcare professional-patient interaction intensity: a cohort study.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1