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Record W3034902685 · doi:10.1016/j.jacep.2020.06.004

Guidance for Rebooting Electrophysiology Through the COVID-19 Pandemic From the Heart Rhythm Society and the American Heart Association Electrocardiography and Arrhythmias Committee of the Council on Clinical Cardiology

2020· article· en· W3034902685 on OpenAlexaff
Mina K. Chung, Thomas F. Deering, Rakesh Gopinathannair, Christine M. Albert, Laurence M. Epstein, Clifford V. Harding, Jodie L. Hurwitz, Courtney Jeffery, Andrew D. Krahn, Fred Kusumoto, Rachel Lampert, Moussa Mansour, Andrea Natale, Kristen K. Patton, Amber Seiler, Maully J. Shah, Paul J. Wang, Andrea M. Russo

Bibliographic record

VenueJACC. Clinical electrophysiology · 2020
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsUniversity of British Columbia
FundersNational Center for Advancing Translational Sciences
KeywordsMedicinePreparednessPandemicCardiac electrophysiologyHealth careHeart RhythmIntensive care medicineImplantable cardioverter-defibrillatorMedical emergencyDiseaseCardiologyCoronavirus disease 2019 (COVID-19)Internal medicineInfectious disease (medical specialty)ElectrophysiologyPolitical science

Abstract

fetched live from OpenAlex

Coronavirus disease 2019 (COVID-19) has presented substantial challenges to patient care and impacted health care delivery, including cardiac electrophysiology practice throughout the globe. Based upon the undetermined course and regional variability of the pandemic, there is uncertainty as to how and when to resume and deliver electrophysiology services for arrhythmia patients. This joint document from representatives of the Heart Rhythm Society, American Heart Association, and American College of Cardiology seeks to provide guidance for clinicians and institutions reestablishing safe electrophysiological care. To achieve this aim, we address regional and local COVID-19 disease status, the role of viral screening and serologic testing, return-to-work considerations for exposed or infected health care workers, risk stratification and management strategies based on COVID-19 disease burden, institutional preparedness for resumption of elective procedures, patient preparation and communication, prioritization of procedures, and development of outpatient and periprocedural care pathways.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.018
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.386
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.001
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.000

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 head0.124
GPT teacher head0.406
Teacher spread0.282 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations15
Published2020
Admission routes1
Has abstractyes

Explore more

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