科学出版物评分——一种总结生物医学出版物证据和数据质量标准的新工具。

IF 1.8 Q3 INFECTIOUS DISEASES
Dieter Bettin, Thomas Maurer, Ferdinand Schlatt, Simon Bettin
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引用次数: 0

摘要

生物医学研究文章的数量每年增加250多万篇,很难跟上时代的步伐。在这项研究中,我们引入了一个标准化的搜索和评估工具来解决这个问题。通过众包,一个庞大的出版物数据库被收集起来。使用标准化的数据输入格式,创造了“科学发表分数”(SPS),可以很容易地汇总具体的发表结果,从而允许对临床问题进行快速和准确的比较。SPS结合了两个质量维度。第一种方法使用英国牛津循证医学中心定义的证据标准来捕捉研究证据的质量。第二种是更细粒度的,考虑对个体和特定结果的统计分析的大小。2014 - 2019年,要求欧洲骨与关节感染学会(EBJIS)专家输入有关假体关节感染的相关出版物数据。对具体结果的数据和证据水平进行平均、总结和排序。共有366份出版物被分为两组:(I)风险因素(例如,与宿主有关的因素、术前和术后问题),有243份出版物;(II)诊断方法(例如,实验室检查、成像方法),有123份出版物。排序后,分别以贫血(平均3.50± SD 0.91)、恶性肿瘤(平均3.17± SD 0.29)和既往异体关节置换术(平均3.00± SD 0.35)的SPS评分计算假体关节感染危险因素得分最高。将完整的SPS排名与2018年肌肉骨骼感染国际共识会议(ICM)确定的排名进行比较,Spearman排名相关系数为0.48,p值为0.0382。SPS最高的诊断方法是抽吸白细胞计数(平均3.15± SD 1.21)、白细胞介素6(平均3.14± SD 1.07)和抽吸(中性粒细胞超过80 %)(平均3.12± SD 0.63)。与ICM排名比较,Spearman排名相关系数为0.91,p值为0.0015。我们的试点研究评估了一种基于源出版物质量对特定结果进行质量评估的新工具。SPS适用于根据对系统评价很重要的证据和数据质量标准对具体结果进行排序。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The scientific publication score - a new tool for summarizing evidence and data quality criteria of biomedical publications.

The number of biomedical research articles increases by over 2.5 million publications each year, making it difficult to stay up to date. In this study, we introduce a standardized search and evaluation tool to combat this issue. Employing crowdsourcing, a large database of publications is gathered. Using a standardized data entry format, coined the "scientific publication score" (SPS), specific publication results can be easily aggregated, thereby allowing fast and accurate comparisons for clinical questions. The SPS combines two quality dimensions. The first captures the quality of evidence of the study using the evidence criteria defined by the Centre for Evidence-Based Medicine, Oxford, UK. The second is more fine-grained and considers the magnitude of statistical analyses on individual and specific results. From 2014 to 2019, experts of the European Bone and Joint Infection Society (EBJIS) were asked to enter data of relevant publications about prosthetic joint infection. Data and evidence levels of specific results were averaged, summarized and ranked. A total of 366 publications were divided into two groups: (I) risk factors (e.g., host-related factors, pre- and postoperative issues) with 243 publications and (II) diagnostic methods (e.g., laboratory tests, imaging methods) with 123 publications. After ranking, the highest score for risk factors of prosthetic joint infection were calculated by the SPS for anemia (mean 3.50 ±  SD 0.91), malignancy (mean 3.17 ±  SD 0.29) and previous alloarthroplasty (mean 3.00 ±  SD 0.35). A comparison of the full SPS ranking with the ranking determined at the 2018 International Consensus Meeting (ICM) on Musculoskeletal Infection resulted in a Spearman rank correlation coefficient of 0.48 and a p  value of 0.0382. The diagnostic methods ranked highest by the SPS were aspirate leucocyte count (mean 3.15 ±  SD 1.21), interleukin 6 (mean 3.14 ±  SD 1.07) and aspirate (neutrophils over 80 %) (mean 3.12 ±  SD 0.63). The comparison to the ICM ranking yielded a Spearman rank correlation coefficient of 0.91 and a p  value of 0.0015. Our pilot study evaluated a new tool for the quality assessment of specific results based on the quality of the source publication. The SPS is suitable for a ranking of specific results by evidence and data quality criteria important for systematic reviews.

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CiteScore
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