Lung preservation: from perfusion to temperature
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".