Wildland fire, air pollution and cardiovascular health: is it time to focus on the microvasculature as a risk assessment tool?
Bibliographic record
Abstract
Climate change favors weather conditions conducive to wildland fires. The intensity and frequency of forest fires are increasing, and fire seasons are lengthening. Exposure of human populations to smoke emitted by these fires increases, thereby contributing to airborne pollution through the emission of gas and particulate matter (PM). The adverse health outcomes associated with wildland fire exposure represent an important burden on the economies and health systems of societies. Even though cardiovascular diseases (CVDs) are the main of cause of the global burden of diseases attributable to PM exposure, it remains difficult to show reliable associations between exposure to wildland fire smoke and cardiovascular disease risk in population-based studies. Optimal health requires a resilient and adaptable network of small blood vessels, namely, the microvasculature. Often alterations of this microvasculature precede the occurrence of adverse health outcomes, including CVD. Biomarkers of microvascular health could then represent possible markers for the early detection of poor cardiovascular outcomes. This review aims to synthesize the current literature to gauge whether assessing the microvasculature can better estimate the cardiovascular impact of wildland fires.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".