Evidence map›Paper›PMID 31481098›Full record

ArticleSystematic reviews2019

An algorithm for the classification of study designs to assess diagnostic, prognostic and predictive test accuracy in systematic reviews.

Tim Mathes, Dawid Pieper

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Systematic reviews, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 9 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed, 9 pooled it
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

30 citing papers in PubMed, 9 syntheses or guidelines pooled it.

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  16. Diagnostic value of circular RNAs as promising hematological biomarkers in acute myeloid leukemia: A systematic review and meta-analysis.Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Tim MathesInstitute for Research in Operative Medicine, Witten/Herdecke University, Ostmerheimer Str. 200, 51109, Cologne, Germany. Tim.Mathes@uni-wh.de.ORCID 0000-0002-5304-1717
Dawid PieperInstitute for Research in Operative Medicine, Witten/Herdecke University, Ostmerheimer Str. 200, 51109, Cologne, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Results of medical tests are the main source to inform clinical decision making. The main information to assess the usefulness of medical tests for correct discrimination of patients are accuracy measures. For the estimation of test accuracy measures, many different study designs can be used. The study design is related to the clinical question to be answered (diagnosis, prognosis, prediction), determines the accuracy measures that can be calculated and it might have an influence on risk of bias. Therefore, a clear and consistent distinction of the different study designs in systematic reviews on test accuracy studies is very important. In this paper, we propose an algorithm for the classification of study designs of test accuracy, that compare the results of an index test (the test to be evaluated) with the results of a reference test (the test whose results are considered as correct/the gold standard) studies in systematic reviews.

Indexed as

AlgorithmsDiagnostic Techniques and ProceduresResearch DesignSensitivity and SpecificitySystematic Reviews as TopicHumansPredictive Value of TestsPrognosisDiagnosis prognosisDiagnostic accuracyPredictionSensitivitySpecificityStudy design classification

Identifiers

PMID31481098
PMCPMC6720081

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Registered trials

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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.