MétaCan
Menu
Back to cohort
Record W2522065893 · doi:10.1016/j.joa.2016.08.001

Erratum to ‘2015 HRS/EHRA/APHRS/SOLAECE expert consensus statement on optimal implantable cardioverter‐defibrillator programming and testing’ [Journal of Arrhythmia 32/1 (2016) 1–28]

2016· erratum· en· W2522065893 on OpenAlexaff
Bruce L. Wilkoff, Laurent Fauchier, Martin K. Stiles, Carlos A. Morillo, Sana M. Al‐Khatib, Jesœs 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 Kück, Germanas Marinskis, Martino Martinelli Filho, Mark A. McGuire, Luis Molina, Ken Okumura, Alessandro Proclemer, Andrea M. Russo, Jagmeet P. Singh, Charles D. Swerdlow, Wee Siong Teo, William Uribe, Sami Viskin, Chun‐Chieh Wang, Shu Zhang

Bibliographic record

VenueJournal of Arrhythmia · 2016
Typeerratum
Languageen
FieldMedicine
TopicCardiac pacing and defibrillation studies
Canadian institutionsMcMaster UniversityPopulation Health Research Institute
Fundersnot available
KeywordsMedicineStatement (logic)Implantable cardioverter-defibrillatorInternal medicine

Abstract

fetched live from OpenAlex

There was an error on the above article. The sentence “Even if a dual-chamber ICD is implanted, dual-chamber discrimination should be programmed only if the atrial lead becomes chronic or if atrial sensing is unreliable” is not correct. It should read: “Even if a dual- chamber ICD is implanted, dual-chamber discrimination should be programmed only if the atrial lead becomes chronic or if atrial sensing is reliable”.

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)0.009
metaresearch head score (Gemma)0.081
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: Other · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.081
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0080.011
Insufficient payload (model declined to judge)0.0230.023

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 head0.027
GPT teacher head0.312
Teacher spread0.285 · 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
GenreOther

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

Citations4
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJournal of ArrhythmiaSame topicCardiac pacing and defibrillation studiesFrench-language works237,207