ArticleBMC medical research methodology2023
No need for a gold-standard test: on the mining of diagnostic test performance indices merely based on the distribution of the test value.
Article in BMC medical research methodology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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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Who cites it
3 citing papers in PubMed.
- Defining an optimal cut-off for subscales without a gold standard: a novel method using inflection points of latent attribute probabilities and raw scores.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2025Article
- Diagnostic tests performance indices: an overview.Biochemia medica · 2025Review
- Data Distribution: Normal or Abnormal?Journal of Korean medical science · 2024Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDiagnostic tests are important in clinical medicine. To determine the test performance indices - test sensitivity, specificity, likelihood ratio, predictive values, etc. - the test results should be compared against a gold-standard test. Herein, a technique is presented through which the aforementioned indices can be computed merely based on the shape of the probability distribution of the test results, presuming an educated guess.
methodsWe present the application of the technique to the probability distribution of hepatitis B surface antigen measured in a group of people in Shiraz, southern Iran. We assumed that the distribution had two latent subpopulations - one for those without the disease, and another for those with the disease. We used a nonlinear curve fitting technique to figure out the parameters of these two latent populations based on which we calculated the performance indices.
resultsThe model could explain > 99% of the variance observed. The results were in good agreement with those obtained from other studies.
conclusionWe concluded that if we have an appropriate educated guess about the distributions of test results in the population with and without the disease, we may harvest the test performance indices merely based on the probability distribution of the test value without need for a gold standard. The method is particularly suitable for conditions where there is no gold standard or the gold standard is not readily available.
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Registered trials
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