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Record W4406992552 · doi:10.1161/str.56.suppl_1.wmp103

Abstract WMP103: Transportation and biomass combustion (including from wildfires) are air pollution sources associated with stroke incidence among women in the California Teachers Study cohort (2000-2018)

2025· article· en· W4406992552 on OpenAlexaff
Sophia Wang, Meredith Franklin, Emily Cauble, Marta Epeldegui, Emma S. Spielfogel, James V. Lacey, Tarik Benmarhnia, Mandy Yao, Jingyuan Wu, Juan Zhao, Cheryl A.M. Anderson, Mitchell S Elkind, Mike Kleeman

Bibliographic record

VenueStroke · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineStroke (engine)Incidence (geometry)Environmental healthAir pollutionBiomass burningCohortPollutionCohort studyMeteorologyAerosolInternal medicineEcologyGeography

Abstract

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Introduction: Ambient air pollution (particulate matter (PM) 2.5, defined as particles < 2.5 microns in diameter) has long been linked to increased stroke risk. However, few studies have described the specific sources or estimated the effects of the PM2.5 constituents responsible for stroke and stroke subtype risk. Hypothesis: We hypothesize that chronic long-term exposure to a major source of air pollution, transportation and its related constituents, will be associated with elevated stroke risk. Methods: In a prospective cohort study of 133,477 women enrolled in the California Teachers Study in 1995-96, we evaluated 110,120 women who resided in California from 2000-2018 (median follow-up=19 years) for residential exposure to primary aerosol concentrations of 8 PM2.5 sources (on- and off-road gasoline vehicles, on- and off-road diesel, biomass combustion, food cooking, aircraft, natural gas combustion), and 11 constituents (including copper, iron, manganese, nitrate, elemental carbon, organic compounds) generated from a 4-km grid chemical transport model. Stroke and stroke subtypes were identified during cohort follow-up with ICD-9 and ICD-10 codes from state hospitalization records (all stroke n=4,348; ischemic stroke n=3,596; hemorrhagic stroke n=752). Cox proportional hazards models were used to estimate hazard ratios (HRs) for associations between the pollutants (average exposure across follow-up) ) with overall stroke and stroke subtypes, adjusted for stroke risk factors. Results: We observed positive associations between PM2.5 exposure from on-road gasoline vehicles with all stroke (HR=1.18, 95% CI=1.11-1.26), ischemic stroke (HR=1.16, 95% CI=1.09-1.26), and hemorrhagic stroke (HR=1.28, 95% CI=1.09-1.49). PM2.5 from off-road diesel was also associated with all stroke (HR=1.21, 95%CI=1.11-1.31), ischemic stroke (HR=1.19, 95% CI=1.09-1.30), and hemorrhagic stroke (HR=1.29, 95% CI=1.06=1.57). PM2.5 from biomass combustion was positively associated with ischemic stroke (HR=1.10, 95% CI=1.02-1.19), but not hemorrhagic stroke. Of PM2.5 constituents evaluated, organic compounds were associated with ischemic stroke (HR=1.14, 95% CI=1.00-1.31). Conclusions: Two major PM2.5 sources, on-road gasoline transportation and off-road diesel, are associated with increased risk of all stroke and stroke subtypes among our female cohort. Long-term exposure to biomass combustion, which includes wildfires, and organic compounds from PM2.5 were linked to ischemic stroke only.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score0.204

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.019
GPT teacher head0.268
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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