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Record W4406442305 · doi:10.1016/j.jtcvs.2024.08.052

The 2024 American Association for Thoracic Surgery expert consensus document: Current standards in donor lung procurement and preservation

2025· article· en· W4406442305 on OpenAlexaff
Jasleen Kukreja, José Luis Campo-Cañaveral de la Cruz, Dirk Van Raemdonck, Edward Cantu, Hiroshi Date, F. D’Ovidio, Matthew G. Hartwig, Jacob A. Klapper, Rosemary F. Kelly, Sandra Lindstedt, Lorenzo Rosso, Lara Schaheen, Michael A. Smith, Bryan A. Whitson, Sahar A. Saddoughi, Marcelo Cypel

Bibliographic record

VenueJournal of Thoracic and Cardiovascular Surgery · 2025
Typearticle
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsUniversity of Toronto
FundersNational Heart, Lung, and Blood InstituteAmerican Association for Thoracic Surgery
KeywordsProcurementMedicineConsensus conferenceAssociation (psychology)General surgeryCardiothoracic surgeryCurrent (fluid)SurgeryIntensive care medicineBusinessInternal medicinePsychologyEngineeringMarketing

Abstract

fetched live from OpenAlex

BACKGROUND: Donor lung procurement and preservation is critical for lung transplantation success. Unfortunately, the large variability in techniques impacts organ utilization rates and transplantation outcomes. Compounding this variation, recent developments in cold static preservation and new technological advances with machine perfusion have increased the complexity of the procedure. The objective of the American Association for Thoracic Surgery (AATS) Clinical Practice Standards Committee (CPSC) expert panel was to make evidence-based recommendations for best practices in donor lung procurement and preservation based on review of the existing literature. METHODS: The AATS CPSC assembled an expert panel of 16 lung transplantation surgeons from 14 centers who developed a consensus document of recommendations. The panel was divided into 7 subgroups covering (1) intraoperative donor assessment, (2) surgical techniques, (3) ex situ static lung preservation methods, (4) hypothermic preservation, (5) normothermic ex vivo lung perfusion (EVLP), (6) donation after circulatory death (DCD) and normothermic regional perfusion, and (7) donor management centers, organ assessment centers, and third-party procurement teams. Following a focused literature review, each subgroup formulated recommendation statements for each subtopic, which were reviewed and further refined using a Delphi process until a 75% consensus was achieved on each final statement by the voting group. RESULTS: The expert panel achieved consensus on 34 recommendations for current best practices in donor lung procurement and preservation both in brain-dead as well as DCD donation. The use of new methods of cold preservation, the role of EVLP, and DCD with and without concomitant heart donation are described in detail. CONCLUSIONS: Consistent and best practices in donor lung procurement and preservation are critical to improve both lung transplantation numbers as well as recipient outcomes. The recommendations described here provide guidance for professionals involved in the care of patients with end-stage lung disease considered for transplantation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.177
metaresearch head score (Gemma)0.178
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.177
Threshold uncertainty score0.938

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1770.178
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0040.007
Bibliometrics0.0120.008
Science and technology studies0.0040.003
Scholarly communication0.0080.004
Open science0.0110.007
Research integrity0.0160.013
Insufficient payload (model declined to judge)0.0070.008

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 head0.026
GPT teacher head0.381
Teacher spread0.356 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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
Published2025
Admission routes1
Has abstractyes

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