Biomarkers for human donor lung assessment during ex vivo lung perfusion
Bibliographic record
Abstract
Lung transplantation remains the only curative treatment option for patients with end-stage lung disease, yet limited donor lung availability and utilization remains a significant obstacle. The ex vivo lung perfusion (EVLP) system allows for the extension of the donor assessment, providing the opportunity to perform advanced assessment on the isolated donor lung under near-physiological conditions. Measuring biomarkers in EVLP perfusate can provide valuable information on the condition of the donor lungs. This review examines biomarkers measured in EVLP perfusate and their ability to predict donor lung utilization and outcomes. Biomarkers in this review can be classified as cytokines, cell death, and endothelial-related molecules, showing potential for clinical application. Some of these biomarkers have also been used to monitor the effects of various therapeutics for donor lung repair or for modification of the EVLP technique. Yet, many limitations persist throughout these studies, which provides the opportunity for extensive future research. The integration of biomarkers with other data collected during EVLP through machine learning and artificial intelligence will lead to automated organ assessment to improve 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".