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Extracorporeal membrane oxygenation bridge to adult heart transplantation

2009· article· en· W1874250894 on OpenAlexaff
Jennifer Chia‐Ying Chung, Hung‐Bin Tsai, Nai‐Kuan Chou, Nai‐Hsin Chi, Shoei‐Shen Wang, Wen‐Je Ko

Bibliographic record

VenueClinical Transplantation · 2009
Typearticle
Languageen
FieldEngineering
TopicMechanical Circulatory Support Devices
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineExtracorporeal membrane oxygenationVentricular assist deviceTransplantationCardiologyCardiopulmonary resuscitationHeart transplantationInternal medicineResuscitationSurgeryHeart failure

Abstract

fetched live from OpenAlex

Extracorporeal membrane oxygenation (ECMO) can rescue some critical patients with circulatory collapse when intra-aortic balloon pump (IABP) and ventricular assist devices (VAD) are not suitable. A subset of these patients can use ECMO for direct bridging, or indirect double bridging via VAD to heart transplantation (HTx). For these patients, we identified risk factors for unsuccessful ECMO bridging, with survival to receiving either HTx or VAD as the measure of success. The characteristics evaluated were age, sex, body mass index, pre-ECMO cardiopulmonary resuscitation (CPR), IABP use, dialysis use, sequential organ failure assessment (SOFA) score, and the etiology of cardiomyopathy. From January 1995 to August 2007, there were 70 adult ECMO patients with the intent to bridge to HTx (male: 55, age: 46 +/- 14 yr). Thirty-one patients (44%) were successful in bridging. A stepwise multivariate logistic regression analysis found that age > 50 yr (p = 0.003), pre-ECMO CPR (p = 0.001) and SOFA score > 10 at ECMO initiation (p = 0.018) were significant independent predictors of unsuccessful bridging. Direct VAD implantation, if possible, is preferable to double bridging in patients over 50 yr. Also, elective ECMO support before hemodynamic deterioration to cardiac arrest or multiple organ dysfunction would improve rates of successful ECMO bridging.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.895
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.040
GPT teacher head0.316
Teacher spread0.276 · 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 designObservational
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

Citations61
Published2009
Admission routes1
Has abstractyes

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