Evidence map›Paper›PMID 37528229›Full record

ArticleArchives of toxicology2023

Uncertainty assessment of proarrhythmia predictions derived from multi-level in silico models.

Karolina Kopańska, Pablo Rodríguez-Belenguer, Jordi Llopis-Lorente, Beatriz Trenor, Javier Saiz, Manuel Pastor

Open access · hybridAbstract read
In one paragraph

Article in Archives of toxicology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.1field-weighted citation impact, top 20% of its field
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

2 citing papers in PubMed, 5 citations in OpenAlex.

  1. Review
  2. Article
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

6 authors at 3 institutions in 1 country.

Karolina Kopańska *Research Programme on Biomedical Informatics (GRIB), Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Hospital del Mar Research Institute, Barcelona, Spain.
Pablo Rodríguez-Belenguer *Research Programme on Biomedical Informatics (GRIB), Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Hospital del Mar Research Institute, Barcelona, Spain.
Jordi Llopis-LorenteCentro de Investigación e Innovación en Bioingeniería (Ci2B), Universitat Politècnica de València, Valencia, Spain.
Beatriz TrenorCentro de Investigación e Innovación en Bioingeniería (Ci2B), Universitat Politècnica de València, Valencia, Spain.
Javier SaizCentro de Investigación e Innovación en Bioingeniería (Ci2B), Universitat Politècnica de València, Valencia, Spain.
Manuel PastorResearch Programme on Biomedical Informatics (GRIB), Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Hospital del Mar Research Institute, Barcelona, Spain. manuel.pastor@upf.edu.ORCID http://orcid.org/0000-0001-8850-1341
Universitat Politècnica de València · ESUniversitat Pompeu Fabra · ESUniversitat de València · ES

Funding

Generalitat Valenciana PROMETEO/ 2020/043Horizon 2020 Framework Programme 101016496Horizon 2020 Framework Programme 777365Horizon 2020 Framework Programme 964537Ministerio de Ciencia, Innovación y Universidades FPU18/01659
6 · The paper itself

Abstract

In silico methods can be used for an early assessment of arrhythmogenic properties of drug candidates. However, their use for decision-making is conditioned by the possibility to estimate the predictions' uncertainty. This work describes our efforts to develop uncertainty quantification methods for the predictions produced by multi-level proarrhythmia models. In silico models used in this field usually start with experimental or predicted IC

Indexed as

Arrhythmias, CardiacHeartBiomarkersComputer SimulationHumansIon ChannelsUncertaintyBiomarkersIon ChannelsDrug-induced ventricular arrhythmiaIn silico toxicologyMachine learningUncertaintyVariability

Identifiers

PMID37528229
PMCPMC10474996
OpenAlexW4385443415

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.