Outcomes of lung transplantation in cystic fibrosis
Bibliographic record
Abstract
PURPOSE OF REVIEW: Lung transplantation (LTX) has transformed care for people with cystic fibrosis (pwCF) suffering from advanced cystic fibrosis lung disease (ACFLD), and it has evolved into an accepted therapy for patients with ACFLD across all ages. We review cystic fibrosis as a major indication for LTX, particularly highlighting outcomes including survival, a changing landscape over time, and factors affecting sequelae following LTX in cystic fibrosis. RECENT FINDINGS: Although some populations such as those undergoing lung retransplantation exhibit inferior posttransplant outcomes, LTX for pwCF provides an excellent long-term survival that has significantly improved over time, likely due to specialized cystic fibrosis center care and recognition of common comorbidities in pwCF post-LTX. There are gaps in post-LTX outcomes for pwCF, including that identified between Canada and the United States, and that seen in adolescents - both of which are likely multifactorial. In particular, the revolution in cystic fibrosis medical therapy with CFTR modulator therapy has resulted in a dramatic decline in programs performing LTX for cystic fibrosis. How durable this effect will remains to be seen. SUMMARY: Overall, LTX remains a well accepted ultimate therapy option in patients with ACFLD if compatible with the individual's goals of care, offering an improved quality of life and maximization of overall survival.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".