Biomass Burning as a Source of Ambient Fine Particulate Air Pollution and Acute Myocardial Infarction
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.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".