Pollution exposure in the first 3 months post transplant is associated with lower baseline FEV1 and higher CLAD risk
Bibliographic record
Abstract
Background Exposure to air pollution post-lung transplant has been shown to decrease graft and patient survival. This study examines the impact of air pollution exposure in the first 3 months post-transplant on baseline (i.e., highest) forced expiratory volume in 1 second (FEV 1 ) achieved and development of chronic lung allograft dysfunction (CLAD). Methods Double-lung transplant recipients ( n = 82) were prospectively enrolled for comprehensive indoor and personal environmental monitoring at 6- and 12-week post transplant and followed for >4 years. Associations between clinical and exposure variables were investigated using an exposomics approach followed by analysis with a Cox proportional hazards model. Multivariable analyses were used to examine the impact of air pollution on baseline % predicted FEV 1 (defined as the average of the 2 highest values post transplant) and risk of CLAD. Results Multivariable analysis revealed a significant inverse relationship between personal black carbon (BC) levels and baseline % FEV 1 . The multivariable model indicated that patients with higher-than-median exposure to BC (>350 ng/m 3 ) attained a baseline % FEV 1 that was 8.8% lower than those with lower-than-median BC exposure ( p = 0.019). Cox proportional hazards model analysis revealed that patients with high personal BC exposure had a 2.4 times higher hazard risk for CLAD than patients with low BC exposure ( p = 0.045). Conclusions Higher personal BC levels during the first 3 months post-transplant decrease baseline FEV 1 and double the risk of CLAD. Strategies to reduce BC exposure early following a lung transplant may help improve lung function and long-term outcomes.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".