Late Breaking Abstract - Multicenter study of bronchial wash-bile acid signature for the diagnosis of aspiration and prediction of chronic lung allograft dysfunction
Bibliographic record
Abstract
Purpose: Bile acids (BA) have been independently validated as a marker of aspiration linked to increased risk of chronic lung allograft dysfunction (CLAD). We aimed to confirm that a large airway bronchial wash (LABW) BA-based aspiration signature will predict CLAD in a large multi-center cohort and will be associated with inflammation. Methods: Two-center retrospective (n=313) cohort included consecutive lung transplant recipients with LABW available at 3 months post-transplant. Fifteen BA and 48 inflammatory mediators were measured by tandem mass spectrometry and by 48-multiplex assay, respectively. Cox regression model, logistic regression and non-parametric statistical analyses ware performed. Results: The median concentration of total BA was 4.25nM (ir:1.9-14.3). Primary conjugated BA were the most abundant species. 8.6nM and 86% were the upper tertile cut-offs used to define high total BA concentration and percent conjugated BA, respectively. Having both high total BA levels and high percentage of conjugated BA independently predicted CLAD (HR 1.9, 95% CI 1.2-2.9, p=0.005) and mortality (HR 2.6, 95% CI 1.7-4.0, p<0.001). Primary BAs strongly correlated with most cytokines. Conclusion: Elevated BA in LABW at 3 months after transplant, particularly conjugated BA, predict mortality and CLAD. Increased levels of primary BA correlate with high cytokines, highlighting their pro-inflammatory role. These findings in a large multi-center cohort support our previous observations that a LABW bile acid-based signature can serve as a diagnostic tool for aspiration and as a predictor of poor long-term outcomes.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".