One-year experience with ex vivo lung perfusion: Preliminary results from a single center
Bibliographic record
Abstract
OBJECTIVE: To enlarge the donor pool for lung transplantation, an increasing number of extended criteria donor lungs are used. However, in more than 50% of multi-organ donors the lungs are not used. Ex vivo lung perfusion offers a unique possibility to evaluate and eventually recondition the injured donor lungs. The aim of our study was to assess the enlargement of the donor pool and the outcome with extended criteria donor lungs after ex vivo lung perfusion. PATIENTS AND METHODS: Data were prospectively collected in our lung transplant database. We compared the results of lung transplants after ex vivo lung perfusion with those after conventional cold static preservation. In total, 11 extended criteria donor lungs processed with ex vivo lung perfusion and 41 cold static preservation lungs transplanted consecutively between May 2016 and May 2017 were evaluated. Normothermic ex vivo lung perfusion was performed according to the Toronto protocol for 4 h. Cold static preservation lungs were stored in low-potassium dextran solution. RESULTS: (P/F) ratios and more X-ray abnormalities. There were no statistically significant differences for pre-donation ventilation time, smoking history, or sex. After reconditioning with ex vivo lung perfusion, 9 out of 11 processed lungs were considered suitable and successfully transplanted. The mean postoperative ventilation time and in-hospital stay were not significantly different in ex vivo lung perfusion and cold static preservation recipients. CONCLUSION: Ex vivo lung perfusion can safely be used in the evaluation of lungs initially considered not suitable for transplantation. The primary outcome was not negatively affected and normothermic ex vivo lung perfusion is a useful tool to increase the usage of potentially transplantable lungs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".