ArticleJournal of general internal medicine2012
Chapter 10: deciding whether to complement a systematic review of medical tests with decision modeling.
Article in Journal of general internal medicine, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Sonoelastography for Testicular Tumor Identification: A Systematic Review and Meta-Analysis of Diagnostic Test Accuracy.Cancers · 2023Review
- Generating and evaluating evidence of the clinical utility of molecular diagnostic tests in oncology.Genetics in medicine : official journal of the American College of Medical Genetics · 2016Article
- A flexible, multifaceted approach is needed in health technology assessment of PET.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2014Article
- A framework for crafting clinical practice guidelines that are relevant to the care and management of people with multimorbidity.Journal of general internal medicine · 2014Review
- Chapter 7: grading a body of evidence on diagnostic tests.Journal of general internal medicine · 2012Article
- Chapter 8: meta-analysis of test performance when there is a "gold standard".Journal of general internal medicine · 2012Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Limited by what is reported in the literature, most systematic reviews of medical tests focus on "test accuracy" (or better, test performance), rather than on the impact of testing on patient outcomes. The link between testing, test results and patient outcomes is typically complex: even when testing has high accuracy, there is no guarantee that physicians will act according to test results, that patients will follow their orders, or that the intervention will yield a beneficial endpoint. Therefore, test performance is typically not sufficient for assessing the usefulness of medical tests. Modeling (in the form of decision or economic analysis) is a natural framework for linking test performance data to clinical outcomes. We propose that (some) modeling should be considered to facilitate the interpretation of summary test performance measures by connecting testing and patient outcomes. We discuss a simple algorithm for helping systematic reviewers think through this possibility, and illustrate it by means of an example.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.