Department of Neurosurgery, Mayo Clinic, Rochester, Minnesota.
Computational Neuroscience Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
Neurosurgery. 2020 Sep 1;87(3):435-441. doi: 10.1093/neuros/nyaa027.
Systematic reviews and meta-analyses in the neurosurgical literature have surged in popularity over the last decade. It is our concern that, without a renewed effort to critically interpret and appraise these studies as high or low quality, we run the risk of the quality and value of evidence-based medicine in neurosurgery being misinterpreted. Correspondingly, we have outlined 4 major domains to target in interpreting neurosurgical systematic reviews and meta-analyses based on the lessons learned by a collaboration of clinicians and academics summarized as 4 pearls. The domains of (1) heterogeneity, (2) modeling, (3) certainty, and (4) bias in neurosurgical systematic reviews and meta-analyses were identified as aspects in which the authors' approaches have changed over time to improve robustness and transparency. Examples of how and why these pearls were adapted were provided in areas of cranial neuralgia, spine, pediatric, and neuro-oncology to demonstrate how neurosurgical readers and writers may improve their interpretation of these domains. The incorporation of these pearls into practice will empower neurosurgical academics to effectively interpret systematic reviews and meta-analyses, enhancing the quality of our evidence-based medicine literature while maintaining a critical focus on the needs of the individual patients in neurosurgery.
在过去的十年中,神经外科学术文献中的系统评价和荟萃分析越来越受欢迎。我们担心的是,如果不重新努力批判性地解释和评估这些研究的高质量或低质量,我们就有可能误解神经外科学循证医学的质量和价值。相应地,我们根据临床医生和学者的合作总结的 4 个要点,概述了 4 个主要领域,以便在解释神经外科系统评价和荟萃分析时针对这些领域。神经外科系统评价和荟萃分析中的(1)异质性、(2)建模、(3)确定性和(4)偏倚被确定为作者的方法随着时间的推移而改变的方面,以提高稳健性和透明度。在颅神经痛、脊柱、儿科和神经肿瘤学等领域提供了这些要点的适应方式和原因的示例,以展示神经外科读者和作者如何提高对这些领域的解释能力。将这些要点纳入实践将使神经外科学者能够有效地解释系统评价和荟萃分析,提高我们循证医学文献的质量,同时保持对神经外科个体患者需求的批判性关注。