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Record W4365444261 · doi:10.1097/mot.0000000000001067

Lung preservation: from perfusion to temperature

2023· review· en· W4365444261 on OpenAlexaboutno aff
Bianca Bromberger, Marek Brzeziński, Jasleen Kukreja

Bibliographic record

VenueCurrent Opinion in Organ Transplantation · 2023
Typereview
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsCold storageLungLung transplantationMedicineIntensive care medicineLung functionPerfusionTransplantationComputer scienceSurgeryCardiologyInternal medicineBiology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: This article will review the evidence behind elements of the lung preservation process that have remained relatively stable over the past decade as well as summarize recent developments in ex-vivo lung perfusion and new research challenging the standard temperature for static cold storage. RECENT FINDINGS: Ex-vivo lung perfusion is becoming an increasingly well established means to facilitate greater travel distance and allow for continued reassessment of marginal donor lungs. Preliminary reports of the use of normothermic regional perfusion to allow utilization of lungs after DCD recovery exist, but further research is needed to determine its ability to improve upon the current method of DCD lung recovery. Also, research from the University of Toronto is re-assessing the optimal temperature for static cold storage; pilot studies suggest it is a feasible means to allow for storage of lungs overnight to allow for daytime transplantation, but ongoing research is awaited to determine if outcomes are superior to traditional static cold storage. SUMMARY: It is crucial to understand the fundamental principles of organ preservation to ensure optimal lung function posttransplant. Recent advances in the past several years have the potential to challenge standards of the past decade and reshape how lung transplantation is performed.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.862
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.172
GPT teacher head0.469
Teacher spread0.297 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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