PM2.5 and NO2 exposure are associated with worse prognosis in IPF: Analysis of the UK PROFILE study
Bibliographic record
Abstract
Introduction: Idiopathic Pulmonary Fibrosis (IPF) is a progressive disease with poor prognosis despite treatment. Air pollution is a global public health concern and has been linked to poor IPF outcomes. The aim of this study was to establish the association between PM2.5 and NO2 exposure and IPF prognosis in a UK population. Methods: PROFILE is prospective observational study of IPF incident cases. Postcodes were used to estimate PM2.5 and NO2 levels through the Atmospheric Composition Analysis Group repository. Exposure levels were based on a five-year mean preceding death or censoring. Median exposure defined high and low categories. Mortality analysis was adjusted for baseline %FVC, %DLCO, age, sex, ethnicity, smoking and deprivation. Results: A total of 524 participants were included. A four-fold greater risk of death was associated with high PM2.5 exposure (HR 4.4, 95%CI 3.55, 5.51). Each 1μg/m3 increase in PM2.5 led to a 65% greater mortality risk (HR 1.65; 95%CI 1.55, 1.83). A risk inflection point was associated with PM2.5 levels that exceeded 11.96μg/m3. Exposure to high NO2 levels was associated with a two-fold higher risk of death (HR 2.21; 95%CI 1.78, 2.76). Each 1ppbv increase in NO2 was associated with 9% higher mortality risk (HR 1.09; 95%CI 1.06, 1.12). PM2.5 and NO2 exposure significantly correlated (R=0.56, p=0.0001) but the association with survival remained independent when adjusting for the each for the other pollutants. Conclusion: In the UK PM2.5 and NO2 exposure were associated with increased mortality in people with IPF, revealing a link between air pollution and fibrosis progression. The findings highlight the need for action towards better air quality in the UK.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".