Systemic Autoimmune Rheumatic Disease Risk: Association With Long‐Term Exposure to Fine Particulate Matter
Bibliographic record
Abstract
Objective Fine particulate matter (PM 2.5 ) is a possible trigger of systemic autoimmune rheumatic diseases (SARDs). We investigated SARDs risk related to long‐term exposure to PM 2.5 and its components (ammonium, black carbon, mineral dust, sea salt, nitrate, sulfate, organic matter), the composition of which may affect toxicity. Methods We assembled an open cohort of Quebec adults (without SARDs) using administrative health data from April 2000 to December 2019. Our SARD case definition included physician billing and hospitalization diagnostic codes for systemic lupus, dermatomyositis, systemic sclerosis, Sjögren disease, and undifferentiated connective tissue disease. Estimates of mean annual PM 2.5 and its components were available from modeling using satellite aerosol optical depth images and ground‐based observations. Exposures were assigned to each resident based on six‐character postal codes, updated over time. Cox models (adjusted for age, sex, year, socioeconomic status, Local Service Network, and urban/rural region) generated hazard ratios (HRs) for PM 2.5 and its seven components. Quantile‐based g‐computational models with similar adjustments were used to estimate marginal HRs for the mixture of PM 2.5 components. Results We studied 7,482,397 Quebec residents. Over 98,039,305 person‐years, 55,267 SARDs cases were identified. Using quantile g‐computational models, the adjusted SARDs HR for a one‐decile increase in PM 2.5 components was 1.01 (95% confidence interval 1.00–1.02). Among the seven components, ammonium contributed most to SARDs risk. Conclusion This large general population cohort study suggests that ambient PM 2.5 (and ammonium in particular) may be associated with SARDs incidence.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".