Ex vivo lung perfusion: a potential platform for molecular diagnosis and ex vivo organ repair
Bibliographic record
Abstract
Lung transplantation is a proven treatment for selected patients with end-stage lung disease. However, the number of patients on the transplant waiting list far exceeds the number of available donor lungs, resulting in waiting list morbidity and mortality. The problem is further exacerbated by the low utilisation rate of available donor lungs, for fear of selecting a damaged lung and the resultant primary graft dysfunction. In the past decade, ex vivo lung perfusion (EVLP) has become part of standard lung transplant clinical practice in Canada and Europe, and it has been shown to improve the usage of available donor lungs by allowing physiological and radiologic evaluation of explanted donor lungs that are considered "marginal". This allows clinicians a second opportunity to decide whether to proceed to transplantation, instead of declining an organ that appears questionable by standard clinical criteria. However there has been much research activity looking at EVLP as a platform for (I) molecular diagnosis, thereby further improving the diagnostic accuracy regarding quality of the donor lung; (II) organ repair, thereby allowing injured donor lungs to become clinically useable. This manuscript summarises some of the preclinical and clinical research from the Toronto group focusing on these promising aspects of EVLP which may further increase the number of useable donor lungs in lung transplantation.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.004 | 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".