Models of Lung Transplant Research: a consensus statement from the National Heart, Lung, and Blood Institute workshop
Bibliographic record
Abstract
Lung transplantation, a cure for a number of end-stage lung diseases, continues to have the worst long-term outcomes when compared with other solid organ transplants. Preclinical modeling of the most common and serious lung transplantation complications are essential to better understand and mitigate the pathophysiological processes that lead to these complications. Various animal and in vitro models of lung transplant complications now exist and each of these models has unique strengths. However, significant issues, such as the required technical expertise as well as the robustness and clinical usefulness of these models, remain to be overcome or clarified. The National Heart, Lung, and Blood Institute (NHLBI) convened a workshop in March 2016 to review the state of preclinical science addressing the three most important complications of lung transplantation: primary graft dysfunction (PGD), acute rejection (AR), and chronic lung allograft dysfunction (CLAD). In addition, the participants of the workshop were tasked to make consensus recommendations on the best use of these complimentary models to close our knowledge gaps in PGD, AR, and CLAD. Their reviews and recommendations are summarized in this report. Furthermore, the participants outlined opportunities to collaborate and directions to accelerate research using these preclinical models.
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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 | 0.028 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".