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Record W2016645010 · doi:10.1093/europace/eun343

Defibrillator shock due to ventricular trigeminy

2008· article· it· W2016645010 on OpenAlexaff
Krishnakumar Nair, Raja J. Selvaraj, V. S. Chauhan

Bibliographic record

VenueEP Europace · 2008
Typearticle
Languageit
FieldMedicine
TopicCardiovascular Effects of Exercise
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsMedicineCardiologyVentricular fibrillationInternal medicineFibrillationBeat (acoustics)Ventricular tachycardiaShock (circulatory)TachycardiaCardiomyopathyAtrial fibrillationHeart failure

Abstract

fetched live from OpenAlex

A 56-year-old lady with arrhythmogenic right ventricular cardiomyopathy had a shock for ventricular trigeminy. The device diagnosed this as ventricular fibrillation because of its binning algorithm, which does not use a consecutive, or a proportional counter. A beat is binned as a fibrillation beat only if the current cycle length is in the fibrillation zone and the running average of the previous four cycle lengths are in the fibrillation or ventricular tachycardia zone. Reprogramming the device into a single detection zone will help prevent shocks in this situation.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.164
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.017

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 head0.013
GPT teacher head0.238
Teacher spread0.225 · 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 designNot applicable
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

Citations2
Published2008
Admission routes1
Has abstractyes

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