Fine particulate matter components and interstitial lung disease in rheumatoid arthritis
Bibliographic record
Abstract
Background Exposure to ambient fine particulate matter with an aerodynamic diameter <2.5 μg·m−3(PM2.5) is a risk factor for pulmonary and systemic autoimmune diseases; however, evidence on which PM2.5chemical components are more harmful is still scant. Our goal is to investigate potential associations between major PM2.5components and interstitial lung disease (ILD) onset in rheumatoid arthritis (RA). Methods New-onset RA subjects identified from a US healthcare insurance database (MarketScan) were followed for new onset of RA-associated ILD (RA-ILD) from 2011 to 2018. Annual concentrations of ambient PM2.5chemical components (i.e.sulfate, nitrate, ammonium, organic matter, black carbon, mineral dust and sea salt) were estimated by combining satellite retrievals with chemical transport modelling and refined by geographically weighted regression. Exposures from 2006 up to 1 year before ILD onset or end of study were assigned to subjects based on their core-based statistical area or metropolitan division codes. A novel time-to-event quantile-based g (generalised)-computation approach was used to estimate potential associations between RA-ILD onset and the exposure mixture of all seven PM2.5chemical components adjusting for age, sex and prior chronic obstructive pulmonary disease (as a proxy for smoking). Results We followed 280 516 new-onset RA patients and detected 2194 RA-ILD cases across 1 394 385 person-years. The adjusted hazard ratio for RA-ILD onset was 1.54 (95% CI 1.47–1.63) per every decile increase in all seven exposures. Ammonium, mineral dust and black carbon contributed more to ILD risk than the other PM2.5components. Conclusion Exposure to components of PM2.5, particularly ammonium, increases ILD risk in RA.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".