Systemic onset juvenile idiopathic arthritis and exposure to fine particulate air pollution.
Bibliographic record
Abstract
OBJECTIVES: Fine particulate matter (PM2.5) is a measurable component of ambient pollution, and positive associations of short-term PM2.5 exposure with the clinical presentation of systemic onset juvenile idiopathic arthritis (SJIA) in young children have been described in a regional cohort. Our objective was to further establish associations between short-term pollution exposures and the reported clinical event of SJIA onset in cases residing from multiple metropolitan regions. METHODS: A case-crossover study design was used to analyse associations of short-term PM2.5 exposures with the event of SJIA symptom onset from cases residing in five metropolitan regions. Time trends, seasonality, month, and weekday were controlled for by matching. Selected exposure windows (to 14 days) of PM2.5 were examined. RESULTS: Positive, statistically significant associations between PM2.5 concentrations and elevated risk of SJIA were not observed. The most positive associations of short-term PM2.5 exposure with SJIA were in children <5.5 years (RR 1.75, 95% CI 0.85-3.62). An ad hoc extended pooled analysis including previously reported cases from Utah's metropolitan areas identified an increased risk of SJIA for children <5.5 years (RR = 1.76, 95% CI 1.07-2.89 per 10 μg/m3 increase in 3-day lagged moving average PM2.5). CONCLUSIONS: In this multi-city, multi-period study small, statistically insignificant PM2.5-SJIA associations are observed. However, as found in prior study, the PM2.5-SJIA association is most suggestive in preschool aged children. Larger numbers of SJIA cases spatially located in geographic areas which experience a greater day to day ambient particulate burden may be required by the analysis to demonstrate effects.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".