Reduced lung fluid club cell secretory protein informs chronic lung allograft dysfunction risk
Bibliographic record
Abstract
Background Lung transplant recipients with lower club cell secretory protein (CCSP) levels in bronchoalveolar lavage fluid (BALF) early post-transplantation are at increased risk for chronic lung allograft dysfunction (CLAD). For CLAD risk stratification, we previously identified a potential risk threshold for reduced CCSP (protein-normalised CCSP <8.63 ng·µg −1 ). Here, we aim to validate this association in an independent patient set from a prospective observational cohort. Methods Total protein and CCSP were quantified in 1481 BALF samples collected over the first post-transplant year from 353 patients (validation cohort). A Cox model tested the association between time to first CCSP <8.63 ng·µg −1 and CLAD. If this threshold did not validate, we prespecified combining the discovery and validation cohorts to rederive a reduced CCSP risk threshold considering a larger number of CLAD events. In a subset, gene expression analyses were performed on allograft biopsies to examine molecular alterations at the time of reduced CCSP. Results BALF CCSP <8.63 ng·µg −1 in the first post-transplant year was not significantly associated with CLAD in the validation cohort (hazard ratio (HR) 1.41; p=0.208). However, in the combined cohort, a dense grid search, including the previously identified threshold of 8.63 ng·µg −1 , revealed that the threshold of 8.63 ng·µg −1 had the largest HR for CLAD. Iterative resampling demonstrated robust reproducibility of the association between BALF CCSP <8.63 ng·µg −1 and CLAD risk across the combined cohort. Biopsies corresponding to CCSP <8.63 ng·µg −1 had a pro-inflammatory profile. Conclusions Early post-transplant reductions in BALF CCSP identify lung recipients at increased CLAD risk and may associate with heightened allograft inflammation.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".