Survival after redo-lung transplantion for CLAD according to phenotype - A multi-center study
Bibliographic record
Abstract
Chronic lung allograft dysfunction (CLAD) remains one of the major complications after lung transplantation (LTx) with no treatment except reLTx in selected candidates. CLAD is however heterogeneous as there are at least 2 phenotypes (Bronchiolitis Obliterans Syndrome, BOS vs Restrictive Allograft Syndrome, RAS). We wanted to evaluate the differential effect of reLTx on patients with BOS or RAS All patients who underwent reLTx for CLAD in 4 LTx centers (Duke, Hannover, Leuven, Toronto) between 2003 and 2013 were retrospectively included. BOS and RAS were distinguished using a combination of pulmonary function, radiology and pathology. Patient variables pre and post reLTx were collected and compared. In a cohort of 133 patients who underwent reLTx, 129 patients could be phenotyped for CLAD, resulting in 85 BOS (65.9%) and 44 RAS (34.1%) patients. There was no difference in gender (p=0.63), age at reLTx (p=0.62), days with first graft (p=0.33) or time to CLAD (p=0.28), while there was a trend for underlying diagnosis (p=0.078). More RAS patients were bridged to reLTx via ECMO (p=0.011). PGD scores 48 hours after reLTx tended to be higher in recipients undergoing ReLTx for RAS (p=0.054). Unadjusted survival after reLTx for RAS was worse compared to BOS (p=0.0004; HR 2.62). Re-development of CLAD was not different (p=0.16). The major causes of death in RAS patients were post-operative complications and redevelopment of CLAD, while CLAD and infection were the most common cause of death in BOS. In this multi-center cohort, patients with RAS have worse survival after reLTx when compared to those with BOS. This raises the question whether RAS patients are suitable candidates for reLTx.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".