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Record W3021752160 · doi:10.1007/s10840-020-00747-5

Recommendations for the organization of electrophysiology and cardiac pacing services during the COVID-19 pandemic

2020· review· en· W3021752160 on OpenAlexaff
Luis Carlos Sáenz, Antonio Miranda, Ricardo Speranza, Ricardo Alkmim Texeira, Ulises Rojel, Andrés Enríquez, Marcio Figuereido

Bibliographic record

VenueJournal of Interventional Cardiac Electrophysiology · 2020
Typereview
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicinePandemicCoronavirus disease 2019 (COVID-19)LimitingMedical emergencyHealth careCardiac electrophysiology2019-20 coronavirus outbreakFace (sociological concept)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Public healthIntensive care medicineDiseaseNursingElectrophysiologyVirologyPathologyInternal medicineOutbreakInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.029
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.026
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.029
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0040.003
Science and technology studies0.0010.001
Scholarly communication0.0040.005
Open science0.0030.003
Research integrity0.0080.008
Insufficient payload (model declined to judge)0.0220.005

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 head0.081
GPT teacher head0.424
Teacher spread0.343 · 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
GenreReview

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

Citations19
Published2020
Admission routes1
Has abstractno

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