Bibliographic record
Abstract
PURPOSE OF REVIEW: Within the last decade, ex-vivo lung perfusion (EVLP) has become a widespread technology used for organ assessment and reconditioning within clinical transplantation. This review aims to offer insights toward future applications and developments in regards to its utility. RECENT FINDINGS: The intervention of EVLP is a well-tolerated method to effectively allow for extended preservation periods. The thoughtful usage of EVLP can therefore be used to optimize operating room logistics and progress lung transplantation toward becoming a more elective procedure. EVLP has also demonstrated itself as an excellent platform for targeted therapies. Prolonged perfusion achieved through further platform stability will allow for time-dependent molecular therapies. Lastly, EVLP allows for the opportunity to perform advanced diagnostics within an isolated setting. Sophistication of point-of-care technologies will allow for accurate predictive measures of transplant outcomes within the platform. SUMMARY: The future of EVLP involves usage of the system as a preservation modality, utilizing advanced diagnostics to predict transplant outcome, and performing therapeutic interventions to optimize organ quality. The generation of clinical data to facilitate and validate these approaches should be performed by transplant centers, which have acquired significant experience using EVLP within their clinical activity.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".