Correlation between BAL CXCR3 chemokines and lung allograft histopathologies: A multicenter study
Bibliographic record
Abstract
The histopathologic diagnosis of acute allograft injury is prognostically important in lung transplantation with evidence demonstrating a strong and consistent association between acute rejection (AR), acute lung injury (ALI), and the subsequent development of chronic lung allograft dysfunction (CLAD). The pathogenesis of these allograft injuries, however, remains poorly understood. CXCL9 and CXCL10 are CXC chemokines induced by interferon-γ and act as potent chemoattractants of mononuclear cells. We hypothesized that these chemokines are involved in the mononuclear cell recruitment associated with AR and ALI. We further hypothesized that the increased activity of these chemokines could be quantified as increased levels in the bronchoalveolar lavage fluid. In this prospective multicenter study, we evaluate the incidence of histopathologic allograft injury development during the first-year post-transplant and measure bronchoalveolar CXCL9 and CXCL10 levels at the time of the biopsy. In multivariable models, CXCL9 levels were 1.7-fold and 2.1-fold higher during AR and ALI compared with "normal" biopsies without histopathology. Similarly, CXCL10 levels were 1.6-fold and 2.2-fold higher during these histopathologies, respectively. These findings support the association of CXCL9 and CXCL10 with episodes of AR and ALI and provide potential insight into the pathogenesis of these deleterious events.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".