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Record W4391108986 · doi:10.3389/ti.2024.12310

The Advent of Semi-Elective Lung Transplantation—Prolonged Static Cold Storage at 10°C

2024· review· en· W4391108986 on OpenAlexaff
Konrad Höetzenecker, Alberto Benazzo, Stefan Schwarz, Shaf Keshavjee, Marcelo Cypel

Bibliographic record

VenueTransplant International · 2024
Typereview
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineCold storageLung transplantationTransplantationLungRandomized controlled trialClinical trialSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Since the early days of clinical lung transplantation the preservation of donor organs has become a fairly standardized procedure and most centers do follow similar processes. This includes the use of low-potassium high dextran flush solutions and static cold storage (SCS) in a cooler filled with ice. Depending on the length of SCS, organs usually arrive at the recipient hospital at a temperature of 0°C-4°C. The question of the optimal storage temperature for donor lung preservation has been revisited as data from large animal experiments demonstrated that organs stored at 10°C experience less mitochondrial damage. Thus, prolonged cold ischemic times can be better tolerated at 10°C-even in pre-damaged organs. The clinical applicability of these findings was demonstrated in an international multi-center observational study including three high-volume lung transplant centers. Total clinical preservation times of up to 24 hrs have been successfully achieved in organs stored at 10°C without hampering primary organ function and short-term outcomes. Currently, a randomized-controlled trial (RCT) is recruiting patients with the aim to compare standard SCS on ice with prolonged SCS protocol at 10°C. If, as anticipated, this RCT confirms data from previous studies, lung transplantation could indeed become a semi-elective procedure.

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 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 score0.934
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.393
Teacher spread0.352 · 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 designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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