The impacts of traffic-related and woodsmoke particulate matter on measures of cardiovascular health: a HEPA filter intervention study
Bibliographic record
Abstract
BACKGROUND: Combustion-generated fine particulate matter (PM2.5) is associated with cardiovascular morbidity. Both traffic-related air pollution and residential wood combustion may be important, but few studies have compared their impacts. OBJECTIVES: To assess and compare effects of traffic-related and woodsmoke PM2.5 on endothelial function and systemic inflammation (C reactive protein, interleukin-6 and band cells) among healthy adults in Vancouver, British Columbia, Canada, using high efficiency particulate air (HEPA) filtration to introduce indoor PM2.5 exposure gradients. METHODS: We recruited 83 healthy adults from 44 homes in traffic-impacted or woodsmoke-impacted areas to participate in this randomised, single-blind cross-over intervention study. PM2.5 concentrations were measured during two consecutive 7-day periods, one with filtration and the other with 'placebo filtration'. Endothelial function and biomarkers of systematic inflammation were measured at the end of each 7-day period. RESULTS: HEPA filtration was associated with a 40% decrease in indoor PM2.5 concentrations. There was no relationship between PM2.5 exposure and endothelial function. There was evidence of an association between indoor PM2.5 and C reactive protein among those in traffic-impacted locations (42.1% increase in C reactive protein per IQR increase in indoor PM2.5, 95% CI 1.2% to 99.5%), but not among those in woodsmoke-impacted locations. There were no associations with interleukin-6 or band cells. CONCLUSIONS: Evidence of an association between C reactive protein and indoor PM2.5 among healthy adults in traffic-impacted areas is consistent with the hypothesis that traffic-related particles, even at relatively low concentrations, play an important role in the cardiovascular effects of the urban PM mixture. TRIAL REGISTRATION NUMBER: http://www.clinicaltrials.gov (NCT01570062).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".