Models of Lung Transplant Research: a consensus statement from the National Heart, Lung, and Blood Institute workshop
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it