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This collection of abstracts presents various methodological advancements in diagnostic and prognostic research, including meta-analysis techniques, machine learning for literature screening, and the PROBAST tool for risk of bias assessment.
Background: The Test-Treatment Pathway has been proposed as a method to link test accuracy to downstream outcomes. By describing the clinical actions before and after testing, it illustrates how a test is positioned in the pathway, relative to other tests and diagnostics, and how the introduction of a new test may change the current diagnostics pathway. However, there is limited practical guidance on how to model such Test-Treatment Pathways. Methods: We selected the Patient -Index test-Comparator -Outcome (PICO) format, as also used elsewhere in evidence-based medicine, as a starting point for building the Test-Treatment Pathways. From there we developed a structured set of triggering questions. We defined these questions based on several brainstorm sessions and iteratively made changes to this basic structure after three rounds of user testing. During the user testing meetings, a pathway was drawn for each specific application. All sessions were recorded both on audio and video.
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Gopalakrishna et al. (2017) studied this question.
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