MétaCan
Menu
Back to cohort
Record W4214908860 · doi:10.1093/europace/euac038

How to use digital devices to detect and manage arrhythmias: an EHRA practical guide

2022· article· en· W4214908860 on OpenAlexaff
Emma Svennberg, Fleur V.Y. Tjong, Andreas Goette, Nazem Akoum, Luigi Di Biase, Pierre Bordachar, Giuseppe Boriani, Haran Burri, Giulio Conte, Jean‐Claude Deharo, Thomas Deneke, Inga Drossart, David Duncker, Janet K. Han, Hein Heidbüchel, Pierre Jaı̈s, Marcio Jansen de Oliveira Figueiredo, Dominik Linz, Gregory Y.H. Lip, Katarzyna Małaczyńska-Rajpold, Manlio F. Márquez, M.C. Ploem, Kyoko Soejima, Martin K. Stiles, Eric Wierda, Kevin Vernooy, Christophe Leclercq, Christian Meyer, Cristiano Pisani, Hui‐Nam Pak, Dhiraj Gupta, Helmut Pürerfellner, Harry J.G.M. Crijns, Edgar Chávez, Stephan Willems, Victor Waldmann, Lukas Dekker, Elaine Y. Wan, Pramesh Kavoor, Mohit K. Turagam, Moritz F. Sinner

Bibliographic record

VenueEP Europace · 2022
Typearticle
Languageen
FieldMedicine
TopicECG Monitoring and Analysis
Canadian institutionsHotel Dieu Hospital
FundersNovo Nordisk FondenAmsterdam University Medical Centers
KeywordsMedicineComputer science

Abstract

fetched live from OpenAlex

In the originally published version of this manuscript, the names of authors Marcio Jansen de Oliveira Figueiredo and Manlio F. Márquez were incorrectly given. Both names have now been corrected online. In addition, the following affiliation was inadvertently omitted for author Manlio F. Márquez: Cardiology, Electrophysiology Service, American British Cowdray Medical Center, Mexico City, México This affiliation has now been added to the online version of the manuscript as affiliation 27 and all subsequent affiliations in this manuscript affected by this change have been updated accordingly.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.049
Threshold uncertainty score0.165

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.025
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.002
Science and technology studies0.0010.001
Scholarly communication0.0050.009
Open science0.0020.004
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0490.094

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.031
GPT teacher head0.312
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations254
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueEP EuropaceSame topicECG Monitoring and AnalysisFrench-language works237,207