Bibliographic record
Abstract
Lung transplantation is a life-saving treatment for patients with end stage lung disease. The imbalance between lung graft supply and recipients has been a serious issue and barrier to successful lung transplantation. Ex vivo lung perfusion is a strategy wherein lungs are perfused and ventilated outside of the body. This technology has emerged as a safe preservation method that also enables the reassessment and reconditioning of marginal lung grafts. Ex vivo lung perfusion has successfully expanded the donor pool and led to greater lung transplant activity worldwide. Furthermore, ex vivo lung perfusion can be used as a platform for advanced diagnostics that enable specific targeted or personalized treatments that can be developed along a bench to bedside pathway leading to safe ex vivo intervention. Recent findings have shown that ex vivo lung perfusion could significantly and safely extend the preservation period, which enables transplant programs further optimization of the logistics around transplantation surgeries, and create a new paradigm whereby donor lungs are assessed at a centralized ex vivo lung perfusion center prior to delivery to a transplant clinic in need. The introduction of ex vivo lung perfusion to clinical lung transplantation has been a major step in the evolution and practice of 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 | 0.001 |
| 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 | 0.002 |
| 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".