Biomass Burning as a Source of Ambient Fine Particulate Air Pollution and Acute Myocardial Infarction
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Bibliographic record
Abstract
BACKGROUND: Biomass burning is an important source of ambient fine particulate air pollution (PM2.5) in many regions of the world. METHODS: We conducted a time-stratified case-crossover study of ambient PM2.5 and hospital admissions for myocardial infarction (MI) in three regions of British Columbia, Canada. Daily hospital admission data were collected between 2008 and 2015 and PM2.5 data were collected from fixed site monitors. We used conditional logistic regression models to estimate odds ratios (ORs) describing the association between PM2.5 and the risk of hospital admission for MI. We used stratified analyses to evaluate effect modification by biomass burning as a source of ambient PM2.5 using the ratio of levoglucosan/PM2.5 mass concentrations. RESULTS: Each 5 µg/m increase in 3-day mean PM2.5 was associated with an increased risk of MI among elderly subjects (≥65 years; OR = 1.06, 95% CI: 1.03, 1.08); risk was not increased among younger subjects. Among the elderly, the strongest association occurred during colder periods (<6.44°C); when we stratified analyses by tertiles of monthly mean biomass contributions to PM2.5 during cold periods, ORs of 1.19 (95% CI: 1.04, 1.36), 1.08 (95% CI: 1.06, 1.09), and 1.04 (95% CI: 1.03, 1.06) were observed in the upper, middle, and lower tertiles (Ptrend = 0.003), respectively. CONCLUSION: Short-term changes in ambient PM2.5 were associated with an increased risk of MI among elderly subjects. During cold periods, increased biomass burning contributions to PM2.5 may modify its association with MI.
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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 | 0.002 | 0.002 |
| 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