Within-city Spatial Variations in Ambient Ultrafine Particle Concentrations and Incident Brain Tumors in Adults
Bibliographic record
Abstract
BACKGROUND: Ambient ultrafine particles (UFPs, <0.1 µm) can reach the human brain, but to our knowledge, epidemiologic studies have yet to evaluate the relation between UFPs and incident brain tumors. METHODS: We conducted a cohort study of within-city spatial variations in ambient UFPs across Montreal and Toronto, Canada, among 1.9 million adults included in multiple cycles of the Canadian Census Health and Environment Cohorts (1991, 1996, 2001, and 2006). UFP exposures (3-year moving averages) were assigned to residential locations using land-use regression models with exposures updated to account for residential mobility within and between cities. We followed cohort members for malignant brain tumors (ICD-10 codes C71.0-C71.9) between 2001 and 2016; Cox proportional hazards models (stratified by age, sex, immigration status, and census cycle) were used to estimate hazard ratios (HRs) adjusting for fine particle mass concentrations (PM2.5), nitrogen dioxide (NO2), and various sociodemographic factors. RESULTS: In total, we identified 1,400 incident brain tumors during the follow-up period. Each 10,000/cm increase in UFPs was positively associated with brain tumor incidence (HR = 1.112, 95% CI = 1.042, 1.188) after adjusting for PM2.5, NO2, and sociodemographic factors. Applying an indirect adjustment for cigarette smoking and body mass index strengthened this relation (HR = 1.133, 95% CI = 1.032, 1.245). PM2.5 and NO2 were not associated with an increased incidence of brain tumors. CONCLUSIONS: Ambient UFPs may represent a previously unrecognized risk factor for incident brain tumors in adults. Future studies should aim to replicate these results given the high prevalence of UFP exposures in urban areas.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".