Imaging Course of Lung Transplantation: From Patient Selection to Postoperative Complications
Bibliographic record
Abstract
Lung transplant is increasingly performed for the treatment of end-stage lung disease. As the number of lung transplants and transplant centers continues to rise, radiologists will more frequently participate in the care of patients undergoing lung transplant, both before and after transplant. Potential donors and recipients undergo chest radiography and CT as part of their pretransplant assessment to evaluate for contraindications to transplant and to aid in surgical planning. After transplant, recipients undergo imaging during the postoperative hospitalization and also in the long-term outpatient setting. Radiologists encounter a wide variety of conditions leading to end-stage lung disease and a myriad of posttransplant complications, some of which are unique to lung transplantation. Familiarity with these pathologic conditions, including their imaging findings and their temporal relationship to the transplant, is crucial to accurate radiologic interpretation. Knowledge of the surgical techniques and expected postoperative appearance prevents confusing normal posttransplant imaging findings with complications. A basic understanding of the indications, contraindications, and surgical considerations of lung transplant aids in imaging interpretation and protocoling and also facilitates communication between radiologists and transplant physicians. Despite medical and surgical advances over the past several decades, lung transplant recipients currently have an average posttransplant life expectancy of only 6.7 years. As members of the transplant team, radiologists can help maximize patient survival and hopefully increase posttransplant life expectancy and quality of life in the coming decades. ©RSNA, 2021 An invited commentary by Bierhals is available online. Online supplemental material is available for this article.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".