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Record W4412766808 · doi:10.1186/s12872-025-05024-9

The lack of evidence-based management in electrical storm: a scoping review

2025· review· en· W4412766808 on OpenAlexaff
Pouya Motazedian, Graeme Prosperi‐Porta, Marie-Eve Mathieu, Nickolas Beauregard, William Knoll, Simon Parlow, Pietro Di Santo, Omar Abdel‐Razek, Richard Jung, Nikola Kolobaric, William Barbour, David Nelson, Trevor Simard, Jacob C. Jentzer, Rebecca Mathew, George A. Wells, F. Daniel Ramirez, Benjamin Hibbert

Bibliographic record

VenueBMC Cardiovascular Disorders · 2025
Typereview
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineAngiologyStormCardiac surgeryInternal medicineGeographyMeteorology

Abstract

fetched live from OpenAlex

Electrical storm (ES) is associated with a significant risk of morbidity and mortality. Despite this, there has been limited research in ES resulting in uncertainty and inconsistency in the management of this life-threatening condition. The objective of this scoping review was to define the current body of literature evaluating pharmacologic and non-pharmacologic therapies used in the management of ES. A comprehensive search of Medline, CENTRAL and Embase was completed on January 11, 2025. Primary studies on pharmacotherapies in ES were included if they reported therapy-related outcomes and included ≥ 5 adult patients. A total of 45 studies met the inclusion criteria. Four studies were randomized control trials (three trials had overlapping cohorts) and the remaining were observational studies. Amiodarone, quinidine, landiolol, isoproterenol, and mexiletine were the most studied medications. The use of sedation for ES was exclusively studied in the context of mechanical ventilation. There was an increase in the number of ES studies over time, but sample sizes remained small and unchanged. Existing evidence to guide the management of ES is predominantly based on small observational studies. High quality data to inform the management of ES is needed.

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)0.002
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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.654
Threshold uncertainty score0.859

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.006
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.123
GPT teacher head0.401
Teacher spread0.278 · 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 designSystematic review
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

Citations2
Published2025
Admission routes1
Has abstractyes

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