Evidence map›Paper›PMID 40507641›Full record

ArticleJournal of clinical medicine2025

Predicting the Higher Energy Need for Effective Defibrillation Using Machine Learning Based on an Animal Model.

Ádám Pál-Jakab, Boldizsár Kiss, Bettina Nagy, Ivetta Boldizsár, István Osztheimer, Erika Rózsa Dévényiné, Violetta Kékesi, Zsolt Lóránt, Béla Merkely, Endre Zima

Abstract read
In one paragraph

Article in Journal of clinical medicine, 2025. 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

10 authors.

Ádám Pál-JakabDepartment of Cardiology, Semmelweis University Heart and Vascular Center, 1122 Budapest, Hungary.ORCID 0000-0002-8625-4815
Boldizsár KissDepartment of Cardiology, Semmelweis University Heart and Vascular Center, 1122 Budapest, Hungary.ORCID 0000-0003-2059-5462
Bettina NagyDepartment of Cardiology, Semmelweis University Heart and Vascular Center, 1122 Budapest, Hungary.ORCID 0000-0002-8334-2385
Ivetta BoldizsárData Science, Eötvös Loránd University, 1053 Budapest, Hungary.
István OsztheimerDepartment of Cardiology, Semmelweis University Heart and Vascular Center, 1122 Budapest, Hungary.ORCID 0000-0002-8209-990X
Erika Rózsa DévényinéInnomed Medical-Medical Developing and Manufacturing Inc., 1146 Budapest, Hungary.
Violetta KékesiDepartment of Cardiology, Semmelweis University Heart and Vascular Center, 1122 Budapest, Hungary.
Zsolt LórántInnomed Medical-Medical Developing and Manufacturing Inc., 1146 Budapest, Hungary.
Béla MerkelyDepartment of Cardiology, Semmelweis University Heart and Vascular Center, 1122 Budapest, Hungary.ORCID 0000-0001-6514-0723
Endre ZimaDepartment of Cardiology, Semmelweis University Heart and Vascular Center, 1122 Budapest, Hungary.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

automated external defibrillator (AED)defibrillation threshold (DFT)machine learning (ML)prediction modelventricular fibrillation (VF)

Identifiers

PMID40507641
PMCPMC12156191

What OpenQuestion holds

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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.