ReviewBiochemia medica2026
EvalTest: a web-based tool for the assessment of diagnostic test performance.
Review in Biochemia medica, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Accurate diagnostic tests are essential in clinical and laboratory medicine. Evaluation of diagnostic test performance requires advanced statistical expertise and the use of specialized or proprietary software. The aim of this work is to develop an open-source R Shiny application for facilitating diagnostic test evaluation by integrating statistical rigor with user-friendly interactivity. Materials and methods: The EvalTest application was developed in the R programming language using the Shiny framework and released as an open-source package on the Comprehensive R Archive Network, with the full source code available on GitHub. In addition, the application is available on a cloud platform and accessible Results: The developed application provides an interactive interface for importing Excel datasets, setting test variable type, selecting test and reference variables, and specifying disease prevalence. It automatically computes key diagnostic performance indicators, including sensitivity, specificity, predictive values, likelihood ratios, accuracy, the Youden index, and the area under the curve (AUC), along with their 95% confidence intervals. EvalTest also generates confusion matrices, receiver operating characteristic (ROC) curves with confidence bands, and identifies the optimal cutoff for quantitative tests. All numerical and graphical outputs can be exported in suitable formats to facilitate reporting and documentation. Conclusions: EvalTest provides an open and reproducible solution for diagnostic test evaluation by computing and visualizing key performance measures with their confidence intervals, supporting its use in clinical and research settings.
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What OpenQuestion holds
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.