Late-breaking abstract: Long-term survival with extracorporeal photochemotherapy in lung transplant rejection patients not responsive to conventional treatment
Bibliographic record
Abstract
Extracorporeal photopheresis (ECP) for chronic lung allograft dysfunction (CLAD) has been reported as beneficial in a few short-term studies. In this retrospective study we report results obtained on 48 CLAD patients treated by off-line ECP for a long time (>8 years) (compared to 58 historical controls) and we explore potential predictors of survival and response. ECP patients, enrolled between February 2003 and December 2013; were n 14 RAS and 34 BOS (grade 1: 58.3%%, grade 2: 20.8%, grade 3: 20.8%), median follow-up was 65 months (cumulative 2284.4 persons-months). Deaths were 20 (41.7%), of which 17 (85%) CLAD-related. Among controls, deaths were 42 (72.4%), of which 32 (76.2%) CLAD-related, over a median 51 months (cumulative 3066.5) (p=0.09). Mortality rate according to failure at 6 months from baseline was significantly higher for historical controls with failure (p<.001) than for all other patient groups including ECP failures. Among ECP patients, FEV1 slope flattened out after the initial months decline (slope -19 ml/month in months 0-6, +4 in months 36-48 and later, p=0.001). RAS was associated to poorer survival, but a pattern of more rapid decline in the previous 6 months was not. No ECP side effects or complications with the off line technique were observed. Long-term ECP treatment for CLAD is safe and reduces FEV1 decline over time also with off line technique; it deserves to be evaluated in a randomized controlled trials, as first line therapy .
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".