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Record W4390007868 · doi:10.3389/frtra.2023.1323387

Review of machine perfusion studies in vascularized composite allotransplant preservation

2023· article· en· W4390007868 on OpenAlexaff
Çağdaş Duru, Felor Biniazan, Nina Hadžimustafić, Andrew D’Elia, Valentina Shamoun, Siba Haykal

Bibliographic record

VenueFrontiers in Transplantation · 2023
Typearticle
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMachine perfusionEx vivoPerfusionLimitingMedicineReperfusion injuryTransplantationCold storageIschemiaComputer scienceSurgeryBiomedical engineeringIn vivoCardiologyBiologyEngineeringMechanical engineeringLiver transplantation

Abstract

fetched live from OpenAlex

The applications of Vascularized composite allotransplantation (VCA) are increasing since the first successful hand transplantation in 1998. However, the abundance of muscle tissue makes VCA's vulnerable to ischemia-reperfusion injury (IRI), which has detrimental effects on the outcome of the procedure, restricting allowable donor-to-recipient time and limiting its widespread use. The current clinical method is Static cold storage (SCS) and this allows only 6 h before irreversible damage occurs upon reperfusion. In order to overcome this obstacle, the focus of research has been shifted towards the prospect of ex-vivo perfusion preservation which already has an established clinical role in solid organ transplants especially in the last decade. In this comprehensive qualitative review, we compile the literature on all VCA machine perfusion models and we aim to highlight the essentials of an ex vivo perfusion set-up, the different strategies, and their associated outcomes.

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: Review · Consensus signal: none
Teacher disagreement score0.618
Threshold uncertainty score0.634

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.057
GPT teacher head0.369
Teacher spread0.312 · 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
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

Citations14
Published2023
Admission routes1
Has abstractyes

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