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Record W1043716041 · doi:10.1155/2015/357498

Lung Transplantation from Donors after Circulatory Death Using Portable <i>Ex Vivo</i> Lung Perfusion

2015· article· en· W1043716041 on OpenAlexaff
Sabin J. Bozso, Vishnu Vasanthan, Jessica G.Y. Luc, Katie Kinaschuk, Darren H. Freed, Jayan Nagendran

Bibliographic record

VenueCanadian Respiratory Journal · 2015
Typearticle
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsCanadian VIGOUR CentreAlberta Biodiversity Monitoring InstituteCanadian Natural ResourcesUniversity of Alberta
Fundersnot available
KeywordsMedicineEx vivoCirculatory systemLungPerfusionLung transplantationTransplantationOrgan donationCardiologySurgeryInternal medicineIn vivo

Abstract

fetched live from OpenAlex

BACKGROUND: Donation after circulatory death is a novel method of increasing the number of donor lungs available for transplantation. Using organs from donors after circulatory death has the potential to increase the number of transplants performed. METHODS: Three bilateral lung transplants from donors after circulatory death were performed over a six-month period. Following organ retrieval, all sets of lungs were placed on a portable ex vivo lung perfusion device for evaluation and preservation. RESULTS: Lung function remained stable during portable ex vivo perfusion, with improvement in partial pressure of oxygen/fraction of inspired oxygen ratios. Mechanical ventilation was discontinued within 48 h for each recipient and no patient stayed in the intensive care unit longer than eight days. There was no postgraft dysfunction at 72 h in two of the three recipients. Ninety-day mortality for all recipients was 0% and all maintain excellent forced expiratory volume in 1 s and forced vital capacity values post-transplantation. CONCLUSION: The authors report excellent results with their initial experience using donors after circulatory death after portable ex vivo lung perfusion. It is hoped this will allow for the most efficient use of available donor lungs, leading to more transplants and fewer deaths for potential recipients on wait lists.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.994

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)0.0010.000

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 head0.049
GPT teacher head0.308
Teacher spread0.259 · 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 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

Citations22
Published2015
Admission routes1
Has abstractyes

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