Fibrotic interstitial lung diseases and air pollution: a systematic literature review
Bibliographic record
Abstract
BACKGROUND: Air pollution is hypothesised to be a risk factor for interstitial lung diseases (ILD). This study systematically reviewed the literature regarding the impact of air pollution on idiopathic pulmonary fibrosis (IPF) and fibrotic interstitial lung diseases (ILD). METHODS: A computer-assisted literature search of electronic databases was performed to identify studies focused on the association between ILDs and air pollution. Other inclusion criteria required that the article had to be: 1) original; 2) a prospective or retrospective study; and 3) fully published in English. Both randomised clinical trials and observational studies were considered. RESULTS: Only seven studies met the inclusion criteria. All studies investigated the relationship between pollution and IPF, except one that dealt with the relationship between pollution and hypersensitivity pneumonitis. Outcome measures included exacerbation of IPF, mortality, disease severity, prevalence of hypersensitivity pneumonitis, progression and incidence of IPF. On the whole, air pollution levels were negatively associated with outcomes in patients with IPF and fibrotic ILD outcome. The heterogeneity in the measurement and reporting of the end-points limited the performance of a quantitative synthesis of data. CONCLUSIONS: This systematic review provides supporting evidence linking exposure to air pollution to poor outcomes in patients with IPF and fibrotic ILD.
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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".