The impacts of traffic-related and woodsmoke particulate matter on measures of cardiovascular health: a HEPA filter intervention study
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it