Pushing the Envelope for Donor Lungs
Bibliographic record
Abstract
The shortage of organ donors remains the major limiting factor in lung transplant, with the number of patients on the waiting list largely exceeding the number of available organ donors. Another issue is the low utilization rate seen in some types of donors. Therefore, novel strategies are continuously being explored to increase the donor pool. Advanced age, smoking history, positive serologies, and size mismatch are common criteria that decrease the rate of use when it comes to organ utilization. Questioning these limitations is one of the purposes of this review. Challenging these limitations by adapting novel donor management strategies could help to increase the rate of suitable lungs for transplantation while still maintaining good outcomes. A second goal is to present the latest advances in organ donation after controlled and uncontrolled cardiac death, and also on how to improve these lungs on ex vivo platforms for assessment and future specific therapies. Finally, pushing the limit of the donor envelope also means reviewing some of the recent improvements made in lung preservation itself, as well as upcoming experimental research fields. In summary, donor lung optimization refers to a global care strategy to increase the total numbers of available allografts, and preserve or improve organ quality without paying the price of early-, mid-, or long-term negative outcomes after 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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".