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Record W2044953804 · doi:10.1016/j.hrcr.2015.02.008

Challenges in the diagnosis and management of idiopathic ventricular fibrillation

2015· article· en· W2044953804 on OpenAlexaff
Jonathan Lipton, George J. Klein, Raymond W. Sy

Bibliographic record

VenueHeartRhythm Case Reports · 2015
Typearticle
Languageen
FieldMedicine
TopicCardiac electrophysiology and arrhythmias
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineVentricular fibrillationAblationCardiologyRefractory (planetary science)Internal medicineCardiac electrophysiologyCatheter ablationAtrial fibrillationManagement of atrial fibrillationIntensive care medicineElectrophysiology

Abstract

fetched live from OpenAlex

The management of idiopathic ventricular fibrillation is challenging. While the exact underlying mechanism remains elusive, the Purkinje system appears to be critically involved in many cases. Medical therapy is largely empirical and ablation may be required in patients with drug-refractory arrhythmia. However, targets for ablation may be difficult to identify when the spontaneous arrhythmia occurs infrequently. In this setting, 12-lead Holter monitoring is extremely useful in identifying a potential target for mapping and ablation prior to the electrophysiology study.

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)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.036
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.001
Science and technology studies0.0030.004
Scholarly communication0.0050.007
Open science0.0030.004
Research integrity0.0050.013
Insufficient payload (model declined to judge)0.0030.003

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 head0.060
GPT teacher head0.303
Teacher spread0.243 · 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 designCase report
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

Citations7
Published2015
Admission routes1
Has abstractyes

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