The association of air pollution with new‐onset epilepsy
Bibliographic record
Abstract
Abstract Objective Air pollution has been associated with certain neurological disorders, but its association with epilepsy has been insufficiently explored. The study's objective was to estimate the association of long‐term exposure to fine particulate matter (PM 2.5 ), nitrogen dioxide (NO 2 ), and ozone (O 3 ) with the risk of new‐onset epilepsy among adults in Ontario, Canada. Methods We used a nested case–control study design and linked health and environmental databases, including Ontario residents ages 18 to 80 as of January 1, 2010, without prior diagnoses of seizures or epilepsy. We identified cases as those who developed epilepsy by December 31, 2016, and matched each with up to five controls on age and sex. We used individual‐ and multi‐pollutant conditional logistic regression models to estimate the associations between interquartile range (IQR) increases in each pollutant and new‐onset epilepsy. Results We included 24 761 cases and 118 692 controls. The median (IQR) pollutant concentrations were 7.9 (1.3) μg/m 3 for PM 2.5 , 9.6 (9.2) ppb for NO 2 , and 42.7 (5.4) ppb for O 3 . In the individual pollutant models, we observed significant associations with epilepsy for PM 2.5 (incident rate ratio [IRR] = 1.055, 95% confidence interval [CI]: 1.034–1.076), NO 2 (IRR = 0.938, 95% CI: 0.903–0.974), and O 3 (IRR = 1.096, 95% CI: 1.074–1.119). In the multi‐pollutant model, we observed significant associations with epilepsy for NO 2 (IRR = 0.928, 95% CI: 0.891–0.965) and O 3 (IRR = 1.090, 95% CI: 1.060–1.121). Although the association for NO 2 was negative overall, the association was positive among individuals 65 and older. Significance PM 2.5 and O 3 may be associated with an increased risk of new‐onset epilepsy. We also observed a negative association for NO 2 . However, residual confounding may have occurred. Future research should continue exploring the associations between specific air pollutants and new‐onset epilepsy.
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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 | 0.001 | 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) | 0.001 | 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".