Rare indications for a lung transplant. A European Society of Thoracic Surgeons survey
Bibliographic record
Abstract
OBJECTIVES: The European Society of Thoracic Surgeons Lung Transplantation Working Group promoted a survey to evaluate overall survival in a large cohort of patients receiving lung transplants for rare pulmonary diseases. METHODS: We conducted a retrospective multicentre study. The primary end point was overall survival; secondary end points were survival of patients with the most common diagnoses in the context of rare pulmonary diseases and chronic lung allograft dysfunction (CLAD)-free survival. Finally, we analysed risk factors for overall survival and CLAD-free survival. RESULTS: Clinical records of 674 patients were extracted and collected from 13 lung transplant centres; diagnoses included 46 rare pulmonary diseases. Patients were followed for a median of 3.1 years. The median survival after a lung transplant was 8.5 years. The median CLAD-free survival was 8 years. The multivariable analysis for mortality identified CLAD as a strong negative predictor [hazard ratio (HR) 6.73)], whereas induction therapy was a protective factor (HR 0.68). The multivariable analysis for CLAD occurrence identified induction therapy as a protective factor (HR 0.51). When we stratified patients by CLAD occurrence in a Kaplan-Meier plot, the survival curves diverged significantly (log-rank test: P < 0.001). Patients with rare diseases who received transplants had chronic rejection rates similar to those of the general population who received transplants. CONCLUSIONS: We observed that overall survival and CLAD-free survival were excellent. We support the practice of allocating lungs to patients with rare pulmonary diseases because a lung transplant is both effective and ethically acceptable.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".