Ex vivo perfusion in lung transplantation and removal of HCV: the next level
Bibliographic record
Abstract
The large gap between high demand and low availability of lungs is still a limiting factor for lung transplantation which leads to important mortality rates on the waiting list. In the last years, with the advent of potent direct-acting antivirals (DAAs), donors carrying active hepatitis C (HCV) infection became an important source in expanding the donor pool. Recent clinical trials exploring different treatment regimens post-transplantation when using HCV-positive abdominal and thoracic organs into HCV-negative recipients have shown encouraging results. Although early data shows no toxicity and similar survival rates when compared to non-HCV organ transplantation, long-term outcomes evaluating the effect of either the transmission of HCV into the recipients or the deliberate use of DAAs to treat the virus remains absent. An important and innovative strategy to overcome this limitation is the possibility of mitigating viral transmission with the use of ex vivo donor organ treatment prior to transplantation. Recent pre-clinical and clinical studies explore the use of ex vivo perfusion and the removal of HCV prior to transplantation with the addition of other innovative therapies, which will be reviewed in this article.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".