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Protecting Cardiovascular Health From Wildfire Smoke

2022· review· en· W4294818544 on OpenAlexaff
Michael Hadley, Sarah B. Henderson, Michael Bräuer, Rajesh Vedanthan

Bibliographic record

VenueCirculation · 2022
Typereview
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsBC Centre for Disease ControlUniversity of British Columbia
Fundersnot available
KeywordsMedicinePsychological interventionEnvironmental healthSmokePublic healthPopulationGlobal healthPathology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.997
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.240
GPT teacher head0.370
Teacher spread0.130 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations67
Published2022
Admission routes1
Has abstractyes

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