Platelet Activation on Lung Function During Ex Vivo Lung Perfusion, Lung Transplantation, and the Role of Antiplatelet Therapy: A Narrative Review
Bibliographic record
Abstract
Background: Ischemia/reperfusion injury after lung transplantation is a significant cause of morbidity. In the realm of ex vivo lung perfusion (EVLP), inflammation, edema formation, and reduced compliance have limited the durability of EVLP. Previous evidence has suggested that platelet activation and thrombosis may play a role in both conditions. Methods: A literature search of PubMed and Embase was conducted, including all articles describing all human or animal investigations of platelet activation or the use of antiplatelet agents in the settings of EVLP or lung transplantation. Articles published from database inception to July 15, 2024, were analyzed. Results: In total, 9 studies were included in the review. Studies on EVLP have found an association between platelet activation and adverse effects on lung function, whereas in lung transplantation, platelet activation appears to play a role in primary graft dysfunction. In both settings, the inhibition of platelets ameliorated these effects. Conclusions: Platelet activation in EVLP and lung transplantation results in distal arterial thrombosis and has been associated with graft dysfunction. The use of antiplatelet agents in the included studies was associated with reduced lung injury and improved lung function on EVLP or during 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".