Left Lung Orthotopic Transplantation in a Juvenile Porcine Model for ESLP
Bibliographic record
Abstract
Lung transplantation is the gold-standard treatment for end-stage lung disease, with over 4,600 lung transplantations performed worldwide annually. However, lung transplantation is limited by a shortage of available donor organs. As such, there is high waitlist mortality. Ex situ lung perfusion (ESLP) has increased donor lung utilization rates in some centers by 15%-20%. ESLP has been applied as a method to assess and recondition marginal donor lungs and has demonstrated acceptable short- and long-term outcomes following transplantation of extended criteria donor (ECD) lungs. Large animal (in vivo) transplantation models are required to validate ongoing in vitro research findings. Anatomic and physiologic differences between humans and pigs pose significant technical and anesthetic challenges. An easily reproducible transplant model would permit the in vivo validation of current ESLP strategies and the preclinical evaluation of various interventions designed to improve donor lung function. This protocol describes a porcine model of orthotopic left lung allotransplantation. This includes anesthetic and surgical techniques, a customized surgical checklist, troubleshooting, modifications, and the benefits and limitations of the approach.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".