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Ventricular arrhythmia storm in the era of implantable cardioverter-defibrillator

2015· review· en· W2287408811 on OpenAlexaff
Khang‐Li Looi, Anthony Tang, Sharad Agarwal

Bibliographic record

VenuePostgraduate Medical Journal · 2015
Typereview
Languageen
FieldMedicine
TopicCardiac pacing and defibrillation studies
Canadian institutionsLondon Health Sciences Centre
Fundersnot available
KeywordsMedicineVentricular tachycardiaImplantable cardioverter-defibrillatorCatheter ablationVentricular fibrillationCardiologyInternal medicineAmiodaroneSudden cardiac deathTachycardiaSedationAtrial fibrillationAnesthesia

Abstract

fetched live from OpenAlex

In the era of widespread use of implantable cardioverter-defibrillators (ICDs) for both primary and secondary prevention of sudden cardiac death, a significant proportion of patients experience episodes of multiple ventricular tachycardia/fibrillation over a short period of time requiring device interventions. The episodes are termed ventricular arrhythmia (VA) or electrical storms. VA storm is a tragic experience for patients, with many psychological consequences. Current management for VA storms remains complex. Acutely, administration of β-blockers, amiodarone and sedation or intubation is generally required to suppress sympathetic tone. Interventional treatment includes catheter ablation and sympathetic blockade by left cardiac sympathetic denervation. Strategies to modify autonomic tone to suppress VAs are the rationale of various novel interventions that have been published in recent studies. All patients with VA storm should be considered for transfer to an experienced high-volume tertiary centre for evaluation and treatment to prevent further recurrence of VA storm.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.904
Threshold uncertainty score0.919

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.061
GPT teacher head0.364
Teacher spread0.303 · 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.

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

Citations16
Published2015
Admission routes1
Has abstractyes

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