Consensus document for the selection of lung transplant candidates: An update from the International Society for Heart and Lung Transplantation
Bibliographic record
Abstract
Tens of thousands of patients with advanced lung diseases may be eligible to be considered as potential candidates for lung transplant around the world each year. The timing of referral, evaluation, determination of candidacy, and listing of candidates continues to pose challenges and even ethical dilemmas. To address these challenges, the International Society for Heart and Lung Transplantation appointed an international group of members to review the literature, to consider recent advances in the management of advanced lung diseases, and to update prior consensus documents on the selection of lung transplant candidates. The purpose of this updated consensus document is to assist providers throughout the world who are caring for patients with pulmonary disease to identify potential candidates for lung transplant, to optimize the timing of the referral of these patients to lung transplant centers, and to provide transplant centers with a framework for evaluating and selecting candidates. In addition to addressing general considerations and providing disease specific recommendations for referral and listing, this updated consensus document includes an ethical framework, a recognition of the variability in acceptance of risk between transplant centers, and establishes a system to account for how a combination of risk factors may be taken into consideration in candidate selection for lung transplantation.
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 | 0.086 | 0.132 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.016 | 0.023 |
| Insufficient payload (model declined to judge) | 0.006 | 0.008 |
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".