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Record W2596020025 · doi:10.1097/mot.0000000000000404

Ex-vivo lung perfusion

2017· review· en· W2596020025 on OpenAlex
Jonathan Yeung, Marcelo Cypel, Shaf Keshavjee

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCurrent Opinion in Organ Transplantation · 2017
Typereview
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsUniversity of TorontoToronto General Hospital
FundersNational Institute of Diabetes and Digestive and Kidney Diseases
KeywordsIntensive care medicineMedicineOrgan transplantationLungProcess (computing)Ex vivoTransplantationSurgeryComputer scienceIn vivoBiologyInternal medicineBiotechnology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Lung evaluation and reconditioning by ex-vivo lung perfusion (EVLP) is becoming increasingly established. We review strategies for broader implementation of this technology to transplant centers worldwide. RECENT FINDINGS: The organ reconditioning hub model is a viable strategy for disseminating EVLP to small and large transplant centers given the well tolerated prolongation of preservation time afforded by EVLP. Regulatory and process issues remain hurdles to be overcome. SUMMARY: EVLP demonstrates promise to increase lung utilization. Organ reconditioning hubs appear to be an efficient method of delivering this promise to all transplant centers, not necessarily only the largest ones. Organ allocation processes will need to adapt to this new paradigm of organ preservation and evaluation. Moreover, regulatory issues will need to be deliberated by the transplant community.

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.889
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
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 head0.237
GPT teacher head0.503
Teacher spread0.266 · 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