Bibliographic record
Abstract
PURPOSE OF REVIEW: The number of patients listed for lung transplantation largely exceeds the number of available transplantable organs because of a shortage of organ donors and a low utilization rate of lungs from those donors who are available. In recent years, novel strategies have been developed to increase the donor lung pool: improved donor management, the use of lungs from donations after cardiac death (DCD), the use of lobar lung living-donors (LLLD) and the use of ex-vivo lung perfusion (EVLP) to assess and repair injured donor lungs. RECENT FINDINGS: An adapted donor management strategy could expand the donor pool up to 20%. DCD lung transplant is an increasing part of the donor pool expansion. Outcomes after controlled DCD seem to be similar to donation after brain death. LLLD transplantation has excellent results for small and critically ill patients. EVLP treatment allows for a significant increase in the rate of suitable lungs and represents an optimal platform for lung reconditioning and specific lung therapies. SUMMARY: A significant increase in the number of available lungs for transplantation is expected in the future because of the wider use of lungs from controlled or uncontrolled DCD and LLLD lungs, and with organ-specific EVLP treatment strategies.
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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 | 0.004 |
| 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) | 0.009 | 0.003 |
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".