Eosinophils in transbronchial biopsies: a predictor of chronic lung allograft dysfunction and reduced survival after lung transplantation — a retrospective single‐center cohort study
Bibliographic record
Abstract
Long-term outcomes after lung transplantation remain inferior to those of other solid organ groups. The significance of eosinophils detected on transbronchial biopsies (TBBx) after lung transplantation and their relationship to long-term outcomes remain unknown. A retrospective single-center cohort study was performed of patients transplanted between January 01, 2001, and July 31, 2018, who had at least 1 TBBx with evaluable parenchymal tissue. Multivariable Cox proportional hazard models were used to assess the associations between eosinophil detection and: all-cause mortality and Chronic Lung Allograft Dysfunction (CLAD). 8887 TBBx reports from 1440 patients were reviewed for the mention of eosinophils in the pathology report. 112 (7.8%) patients were identified with eosinophils on at least one TBBx. The median (95% CI) survival time for all patients was 8.28 (7.32-9.31) years. Multivariable analysis, adjusted for clinical variables known to affect post-transplant outcomes, showed that the detection of eosinophils was independently associated with an increased risk of death (HR 1.51, 95% CI 1.24-1.85, p < 0.01) and CLAD (HR 1.35, 95% CI 1.07-1.70, P = 0.01). Eosinophils detected in TBBx are associated with an increased risk of CLAD and death. There may be benefit in specifically reporting the presence of eosinophils in TBBx reports and incorporating their presence in clinical decision-making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.001 | 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".