Bahor Zsanett, Liao Jing, Macleod Malcolm R, Bannach-Brown Alexandra, McCann Sarah K, Wever Kimberley E, Thomas James, Ottavi Thomas, Howells David W, Rice Andrew, Ananiadou Sophia, Sena Emily
Centre for Clinical Brain Sciences, University of Edinburgh, Chancellor's Building, Little France Crescent, Edinburgh, U.K.
SYRCLE, Nijmegen Institute for Health Sciences, Radboud University Medical Center, Nijmegen, Netherlands.
Clin Sci (Lond). 2017 Oct 12;131(20):2525-2532. doi: 10.1042/CS20160722. Print 2017 Oct 15.
Findings from research may be less reliable where studies do not report measures to reduce risks of bias. The experimental stroke community has been at the forefront of implementing changes to improve reporting, but it is not known whether these efforts are associated with continuous improvements. Our aims here were firstly to validate an automated tool to assess risks of bias in published works, and secondly to assess the reporting of measures taken to reduce the risk of bias within recent literature for two experimental models of stroke.
We developed and used text analytic approaches to automatically ascertain reporting of measures to reduce risk of bias from full-text articles describing animal experiments inducing middle cerebral artery occlusion (MCAO) or modelling lacunar stroke.
Compared with previous assessments, there were improvements in the reporting of measures taken to reduce risks of bias in the MCAO literature but not in the lacunar stroke literature. Accuracy of automated annotation of risk of bias in the MCAO literature was 86% (randomization), 94% (blinding) and 100% (sample size calculation); and in the lacunar stroke literature accuracy was 67% (randomization), 91% (blinding) and 96% (sample size calculation).
There remains substantial opportunity for improvement in the reporting of animal research modelling stroke, particularly in the lacunar stroke literature. Further, automated tools perform sufficiently well to identify whether studies report blinded assessment of outcome, but improvements are required in the tools to ascertain whether randomization and a sample size calculation were reported.
在研究未报告降低偏倚风险的措施时，研究结果可能不太可靠。实验性中风研究领域一直处于实施改进报告措施的前沿，但尚不清楚这些努力是否带来持续改进。我们在此的目的，一是验证一种自动化工具，用于评估已发表研究中的偏倚风险，二是评估近期文献中针对两种中风实验模型所采取的降低偏倚风险措施的报告情况。
我们开发并使用文本分析方法，从描述诱导大脑中动脉闭塞（MCAO）的动物实验或腔隙性中风模型的全文文章中，自动确定降低偏倚风险措施的报告情况。
与之前的评估相比，MCAO文献中降低偏倚风险措施的报告有所改进，但腔隙性中风文献中没有。MCAO文献中偏倚风险自动标注的准确率为随机化86%、盲法94%、样本量计算100%；腔隙性中风文献中的准确率分别为随机化67%、盲法91%、样本量计算96%。
中风动物研究模型的报告仍有很大改进空间，尤其是在腔隙性中风文献方面。此外，自动化工具在识别研究是否报告了对结果的盲法评估方面表现良好，但在确定是否报告了随机化和样本量计算方面，该工具仍需改进。
