Bibliographic record
Abstract
PURPOSE OF REVIEW: The number of patients listed for lung transplantation largely exceeds the number of available transplantable organs because of both a shortage of organ donors and a low utilization rate of lungs from those donors. Two major innovations in recent years include the use of lungs from donations after cardiac death (DCD) and the use of ex-vivo lung perfusion (EVLP) to assess and improve injured donor lungs. RECENT FINDINGS: DCD lung transplants now account for about 20% of lung transplants in many centres and outcomes after transplantation have been excellent with this source of donation. Clinical experience using EVLP has shown the method to be well tolerated and allow for reassessment and improvement in function from high-risk donor lungs. When these lungs were transplanted, low rates of primary graft dysfunction were achieved and long-term survival was comparable with standard transplantation. Preclinical studies have shown a great potential of EVLP as a platform for the delivery of novel therapies to repair injured donor lungs. SUMMARY: A significant increase on the number of available lungs for transplantation is expected in the coming years with the wider use of DCD lungs and with organ-specific ex-vivo 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".