Protecting Cardiovascular Health From Wildfire Smoke
Bibliographic record
Abstract
Wildfire smoke is a rapidly growing threat to global cardiovascular health. We review the literature linking wildfire smoke exposures to cardiovascular effects. We find substantial evidence that short-term exposures are associated with key cardiovascular outcomes, including mortality, hospitalization, and acute coronary syndrome. Wildfire smoke exposures will continue to increase over the majority of Earth's surface. For example, the United States alone has experienced a 5-fold increase in annual area burned since 1972, with 82 million individuals estimated to be exposed to wildfire smoke by midcentury. The associated rise in excess morbidity and mortality constitutes a growing global public health crisis. Fortunately, the effect of wildfire smoke on cardiovascular health is modifiable at the individual and population levels through specific interventions. Health systems therefore have an opportunity to help safeguard patients from smoke exposures. We provide a roadmap of evidence-based interventions to reduce risk and protect cardiovascular health. Key interventions include preparing health systems for smoke events; identifying and educating vulnerable patients; reducing outdoor activities; creating cleaner air environments; using air filtration devices and personal respirators; and aggressive management of chronic diseases and traditional risk factors. Further research is needed to test the efficacy of interventions on reducing cardiovascular outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".