The Changing Transplant Landscape in the Era of Elexacaftor/Tezacaftor/Ivacaftor: A Word of Caution
Bibliographic record
Abstract
BACKGROUND: Elexacaftor/tezacaftor/ivacaftor (ETI) has dramatically changed the landscape of cystic fibrosis (CF) care, including in those who require lung transplantation. The objectives of the study were to describe the cohort demographics and outcomes of primary lung transplant recipients before and after the availability of ETI. METHODS: This is a descriptive study of lung transplants performed at the Toronto Lung Transplant Program for CF during two time periods: 2019 (pre-ETI era) and 2021-2023 (post-ETI era). All subjects were referred from the Adult CF program at St. Michael's Hospital, Toronto. Data were obtained from chart review and the Toronto Lung Transplant database. The Kaplan-Meier method was used to estimate survival probability at 1 year post-transplant. RESULTS: There were 22 lung transplants performed in 2019 (19 [86.4%] primary and 3 [13.6%] re-transplants) compared to 11 lung transplants (8 [72.7%] primary and 3 [27.3%] re-transplants) in the post-ETI era. In primary transplant recipients, median age was 29.4 years (Range 18.6-67.6 years) in 2019 compared to 30.0 years (Range 19.1-64.0 years) in 2021-2023. In the post-ETI era, none of the individuals had a deltaF508 variant, compared to 84% in 2019. One-year survival probability was lower in the post-ETI era (62.5% vs. 84.2%, respectively). CONCLUSION: Lung transplant recipients in the post-ETI era were more complex with high-risk characteristics and had worse post-transplant outcomes. This study highlights the importance of further investigation to better understand the impact of ETI on transplant referral patterns, recipient characteristics, and post-transplant outcomes in the CF population.
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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 | 0.002 | 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".