Particulate Matter Associations with Lung Function in Pulmonary Sarcoidosis
Bibliographic record
Abstract
Abstract Rationale Sarcoidosis is a multisystem disease with pulmonary manifestations in >90% of patients. Environmental exposures are associated with sarcoidosis incidence, but the impact on clinical outcomes remains understudied. Objectives To evaluate the association of exposures to ambient particulate matter ≤2.5 μm in diameter (PM2.5) with lung function outcomes in pulmonary sarcoidosis. Methods PM2.5 and constituent exposures were obtained by matching monthly satellite-derived hybrid measurements to each patient’s residential address obtained from the time of enrollment, averaged over 5 years before registry enrollment and censoring. Linear models evaluated associations of pollutants with baseline lung function (FEV1, FVC, FEV1/FVC ratio, and Dl CO). Linear mixed effects models analyzed associations of pollutants with rates of lung function change (FEV1, FVC, and Dl CO change per year of follow-up). Measurements and Main Results Two prospectively enrolled cohorts of mostly middle-aged, White, and nonsmoking adults with specialist-diagnosed pulmonary sarcoidosis were used. The U.S. cohort (n = 400) experienced higher 5-year preenrollment median PM2.5 exposures (12.3 μg/m3) than the Canadian cohort (n = 112) (8.0 μg/m3). Each 1-μg/m3 increase in PM2.5 was associated with 0.93% predicted lower baseline FEV1 (95% confidence interval, −1.76 to −0.10; P = 0.03) and 1.53% predicted lower FVC (95% confidence interval, −2.26 to −0.79; P < 0.001) in the U.S. cohort, but the associations were not significant in the Canadian cohort. PM2.5, sulfate, nitrate, and ammonium were associated with accelerated FEV1, FVC, and Dl CO decline in the U.S. cohort. Conclusions PM2.5 was associated with worse pulmonary disease severity and progression in a higher-exposure cohort, highlighting the importance of exposure disparities in this population.
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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) | 0.000 | 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 | 0.000 |
| 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".