Airway pepsinogen A4 identifies lung transplant recipients with microaspiration and predicts chronic lung allograft dysfunction
Bibliographic record
Abstract
BACKGROUND: Aspiration is a known risk factor for adverse outcomes post-lung transplantation. Airway bile acids are the gold-standard biomarker of aspiration; however, they are released into the duodenum and likely reflect concurrent gastrointestinal dysmotility. Previous studies investigating total airway pepsin have found conflicting results on its relationship with adverse outcomes post-lung transplantation. These studies measured total pepsin and pepsinogen in the airways. Certain pepsinogens are constitutively expressed in the lungs, while others, such as pepsinogen A4 (PGA4), are not. We sought to evaluate the utility of measuring airway PGA4 as a biomarker of aspiration and predictor of adverse outcomes in lung transplant recipients (LTRs) early post-transplant. METHODS: Expression of PGA4 was compared to other pepsinogens in lung tissue. Total pepsin and PGA4 were measured in large airway bronchial washings and compared to preexisting markers of aspiration. Two independent cohorts of LTRs were used to assess the relationship between airway PGA4 and chronic lung allograft dysfunction (CLAD). Changes to airway PGA4 after antireflux surgery were assessed in a third cohort of LTRs. RESULTS: PGA4 was expressed in healthy human stomach but not lung. Airway PGA4, but not total pepsin, was associated with aspiration. Airway PGA4 was associated with an increased risk of CLAD in two independent cohorts of LTRs. Antireflux surgery was associated with reduced airway PGA4. CONCLUSIONS: Airway PGA4 is a marker of aspiration that predicts CLAD in LTRs. Measuring PGA4 at surveillance bronchoscopies can help triage high-risk LTRs for anti-reflux surgery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| 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".