Fernanda J P Teixeira, Bakhtawar Ahmad, Viktoriya Gibatova, Pouya A Ameli, Ivan da Silva, Thiago Carneiro, William Roth, Jenna L Ford, Terry Kit Selfe, David M Greer, Katharina M Busl, Carolina B Maciel
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引用次数: 1
Abstract
Self-fulfilling prophecy bias occurs when a perceived prognosis leads to treatment decisions that inherently modify outcomes of a patient, and thus, overinflate the prediction performance of prognostic methods. The goal of this series of systematic reviews is to characterize the extent to which neuroprognostic studies account for the potential impact of self-fulfilling prophecy bias in their methodology by assessing their adequacy of disclosing factors relevant to this bias.
Methods: Studies evaluating the prediction performance of neuroprognostic tools in cardiac arrest, malignant ischemic stroke, traumatic brain injury, subarachnoid hemorrhage, and spontaneous intracerebral hemorrhage will be identified through PubMed, Cochrane, and Embase database searches. Two reviewers blinded to each other's assessment will perform screening and data extraction of included studies using Distiller SR and following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We will abstract data pertinent to the methodology of the studies relevant to self-fulfilling prophecy bias.
Results: We will conduct a descriptive analysis of the data. We will summarize the reporting of mortality according to timing and mode of death, rates of exposure to withdrawal of life-sustaining therapy, reasoning behind limitations of supportive care, systematic use of standardized neuroprognostication algorithms and whether the tool being investigated is part of such assessments, and blinding of treatment team to results of neuroprognostic test being evaluated.
Conclusions: We will identify if neuroprognostic studies have been transparent in their methodology to factors that affect the self-fulfilling prophecy bias. Our results will serve as the foundation for standardization of neuroprognostic study methodologies by refining the quality of the data derived from such studies.