Gaseous Air Pollutants and Lung Function in Fibrotic Interstitial Lung Disease (fILD): Evaluation of Different Spatial Analysis Approaches
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Gaseous pollutants such as CO, NO 2, O 3, and SO 2 are linked to adverse clinical outcomes in patients with fibrotic interstitial lung diseases (fILDs), particularly idiopathic pulmonary fibrosis. However, the effect of various exposure estimation methods on these findings remains unclear. This study aims to evaluate three spatial approaches─nearest neighbor (NN), inverse distance weighting (IDW), and Kriging─for estimating gaseous pollutant exposures and to assess how these methods affect health outcome estimates in fILD patients. A 10-fold cross-validation showed that Kriging had the lowest prediction error compared to NN and IDW, with RMSE for CO = 0.43 ppm (11%), O 3 = 5.9 ppb (14%), SO 2 = 2.7 ppb (12%), and NO 2 = 7.6 ppb (9%), respectively. Kriging also excelled over other methods across wide spatial and temporal ranges, showing the highest spatial R 2 for CO and O 3 and the highest temporal R 2 for SO 2 and NO 2 . In a large cohort of patients with fILD, higher levels of CO, SO 2, and NO 2 exposure were associated with lower pulmonary function. The magnitude of association and its precision were higher in SO 2 and CO estimated by the Kriging method. This study underscores Kriging as a robust method for estimating gaseous pollutant levels and offers valuable insights for future epidemiological studies.
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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 | 0.002 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".