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Record W4406480908 · doi:10.1177/02676591241313167

Considering veno-venous extracorporeal membrane oxygenation as a first-line strategy for rewarming in accidental hypothermia complicated by cardiac arrest - a case series

2025· article· en· W4406480908 on OpenAlexaff
Hossein Shayan, Derek Gunning, Michelle Mozel, Kamen Valchanov

Bibliographic record

VenuePerfusion · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsFraser HealthUniversity of British Columbia
Fundersnot available
KeywordsMedicineExtracorporeal membrane oxygenationHypothermiaAnesthesiaCardiology

Abstract

fetched live from OpenAlex

Severe accidental hypothermia can lead to cardiac arrest. The most efficient method of resuscitating and warming is by ECMO (Extracorporeal Membrane Oxygenation). While the convention is to use VA ECMO (Veno Arterial ECMO), using VV ECMO (Veno Venous ECMO) in which the blood is returned directly into the right ventricle could be an alternative and lead to conversion to life sustaining cardiac rhythm. In this article we present our case series of ECMO for resuscitation of accidental hypothermia complicated by cardiac arrest. We used VV ECMO for 4 patients; in 3 of them ROSC (Return of Spontaneous Circulation) was successfully achieved. We also discuss the potential advantages of VV ECMO and VA ECMO in this setting and present our algorithm for management.

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)0.000
metaresearch head score (Gemma)0.002
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: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.020
GPT teacher head0.298
Teacher spread0.279 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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