Ex vivo lung perfusion for donor lung assessment and repair: a review of translational interspecies models
Bibliographic record
Abstract
For patients with end-stage lung disease, lung transplantation is a lifesaving therapy. Currently however, the number of patients who require a transplant exceeds the number of donor lungs available. One of the contributing factors to this is the conservative mindset of physicians who are concerned about transplanting marginal lungs due to the potential risk of primary graft dysfunction. Ex vivo lung perfusion (EVLP) technology has allowed for the expansion of donor pool of organs by enabling assessment and reconditioning of these marginal grafts before transplant. Ongoing efforts to optimize the therapeutic potential of EVLP are underway. Researchers have adopted the use of different large and small animal models to generate translational preclinical data. This includes the use of rejected human lungs, pig lungs, and rat lungs. In this review, we summarize some of the key current literature studies relevant to each of the major EVLP model platforms and identify the advantages and disadvantages of each platform. The review aims to guide investigators in choosing an appropriate species model to suit their specific goals of study, and ultimately aid in translation of therapy to meet the growing needs of the patient population.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| 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) | 0.003 | 0.001 |
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".