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Record W4386220092 · doi:10.3390/jcm12175576

Extracorporeal Membrane Oxygenation (VA-ECMO) in Management of Cardiogenic Shock

2023· review· en· W4386220092 on OpenAlexaff
Klaudia J. Koziol, Ameesh Isath, Shiavax J. Rao, Vasiliki Gregory, Suguru Ohira, Sean van Diepen, Roberto Lorusso, Chayakrit Krittanawong

Bibliographic record

VenueJournal of Clinical Medicine · 2023
Typereview
Languageen
FieldEngineering
TopicMechanical Circulatory Support Devices
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineCardiogenic shockExtracorporeal membrane oxygenationIntensive care medicineExtracorporealRandomized controlled trialShock (circulatory)CardiologyInternal medicineMyocardial infarction

Abstract

fetched live from OpenAlex

Cardiogenic shock is a critical condition of low cardiac output resulting in insufficient systemic perfusion and end-organ dysfunction. Though significant advances have been achieved in reperfusion therapy and mechanical circulatory support, cardiogenic shock continues to be a life-threatening condition associated with a high rate of complications and excessively high patient mortality, reported to be between 35% and 50%. Extracorporeal membrane oxygenation can provide full cardiopulmonary support, has been increasingly used in the last two decades, and can be used to restore systemic end-organ hypoperfusion. However, a paucity of randomized controlled trials in combination with high complication and mortality rates suggest the need for more research to better define its efficacy, safety, and optimal patient selection. In this review, we provide an updated review on VA-ECMO, with an emphasis on its application in cardiogenic shock, including indications and contraindications, expected hemodynamic and echocardiographic findings, recommendations for weaning, complications, and outcomes. Furthermore, specific emphasis will be devoted to the two published randomized controlled trials recently presented in this setting.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.936
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.185
GPT teacher head0.432
Teacher spread0.247 · 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 designOther design
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

Citations55
Published2023
Admission routes1
Has abstractyes

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