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Record W2178215348 · doi:10.1093/europace/euv411

2015 HRS/EHRA/APHRS/SOLAECE expert consensus statement on optimal implantable cardioverter-defibrillator programming and testing

2015· article· en· W2178215348 on OpenAlexaff
Bruce L. Wilkoff, Laurent Fauchier, Martin K. Stiles, Carlos A. Morillo, Sana M. Al‐Khatib, Jesús Almendral, Luis Aguinaga, Ronald D. Berger, Alejandro Cuesta, James P. Daubert, Sérgio Dubner, Kenneth A. Ellenbogen, N.A. Mark Estes, Guilherme Fenelon, Fermin C. García, Maurizio Gasparini, David E. Haines, Jeff S. Healey, Jodie L. Hurtwitz, Roberto Keegan, Christof Kolb, Karl-Heinz Kuck, Germanas Marinskis, Mark A. McGuire, Luis Molina, Ken Okumura, Alessandro Proclemer, Andrea Russo, Jagmeet P. Singh, Charles D. Swerdlow, Wee Siong Teo, William Uribe, Sami Viskin, Chun‐Chieh Wang, Shu Zhang

Bibliographic record

VenueEP Europace · 2015
Typearticle
Languageen
FieldMedicine
TopicCardiac pacing and defibrillation studies
Canadian institutionsMcMaster UniversityPopulation Health Research Institute
Fundersnot available
KeywordsMedicineImplantable cardioverter-defibrillatorDefibrillationHeart RhythmBradycardiaVentricular tachycardiaVentricular fibrillationTachycardiaInternal medicineIntensive care medicineHeart rateBlood pressure

Abstract

fetched live from OpenAlex

International audience

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)0.100
metaresearch head score (Gemma)0.135
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.100
Threshold uncertainty score0.527

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1000.135
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.008
Bibliometrics0.0070.004
Science and technology studies0.0030.002
Scholarly communication0.0070.003
Open science0.0080.006
Research integrity0.0150.010
Insufficient payload (model declined to judge)0.0110.008

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 head0.087
GPT teacher head0.341
Teacher spread0.254 · 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 designTheoretical or conceptual
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

Citations242
Published2015
Admission routes1
Has abstractyes

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