{"title":"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。","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":null,"pages":null},"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":"{\"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\":null,\"pages\":null},\"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}","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
摘要
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。
PubMed captures more fine-grained bibliographic data on scientific commentary than Web of Science: a comparative analysis.
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.