Flexible Risk Evidence Combination Rules in Breast Cancer Precision Therapy.

Michael Kenn, Rudolf Karch, Christian F Singer, Georg Dorffner, Wolfgang Schreiner

Abstract read

In one paragraph

Article in Journal of personalized medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Michael KennSection of Biosimulation and Bioinformatics, Center for Medical Statistics, Informatics and Intelligent Systems (CeMSIIS), Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria.ORCID 0000-0002-8998-8005

Rudolf KarchSection of Biosimulation and Bioinformatics, Center for Medical Statistics, Informatics and Intelligent Systems (CeMSIIS), Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria.ORCID 0000-0003-1541-7215

Christian F SingerTranslational Gynecology Group, Department of Obstetrics and Gynecology Comprehensive Cancer Center, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria.ORCID 0000-0003-1294-0027

Georg DorffnerSection for Artificial Intelligence, Center for Medical Data Science, Medical University of Vienna, Währingerstraße 25a, 1090 Vienna, Austria.ORCID 0000-0002-3181-2576

Wolfgang SchreinerSection of Biosimulation and Bioinformatics, Center for Medical Statistics, Informatics and Intelligent Systems (CeMSIIS), Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria.ORCID 0000-0003-1910-5788

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Evidence theory by Dempster-Shafer for determination of hormone receptor status in breast cancer samples was introduced in our previous paper. One major topic pointed out here is the link between pieces of evidence found from different origins. In this paper the challenge of selecting appropriate ways of fusing evidence, depending on the type and quality of data involved is addressed. A parameterized family of evidence combination rules, covering the full range of potential needs, from emphasizing discrepancies in the measurements to aspiring accordance, is covered. The consequences for real patient samples are shown by modeling different decision strategies.

Indexed as

breast cancerdata scienceDempster-Shafer theoryevidence theoryhormone receptor statusmathematical oncologypersonalized medicineprecision medicinetheory of belief functions

Identifiers

PMID36675780

DOI10.3390/jpm13010119

PMCPMC9865003

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

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.