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Record W2923783136 · doi:10.13075/ijomeh.1896.01326

A systematic review of the health impacts of occupational exposure to wildland fires

2019· review· en· W2923783136 on OpenAlexaff
E.H. de Groot, Alexa Caturay, Yasmin Khan, Ray Copes

Bibliographic record

VenueInternational Journal of Occupational Medicine and Environmental Health · 2019
Typereview
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsNOSM UniversityQueen's UniversityUniversity of TorontoPublic Health Ontario
Fundersnot available
KeywordsEnvironmental healthMedicineOccupational safety and healthOccupational exposureLung functionLung cancerPathologyLungInternal medicine

Abstract

fetched live from OpenAlex

The aim of the paper is to summarize the evidence of health impacts of occupational exposure to wildland fires. The authors searched 3 databases for relevant articles and screened the results. After full-text review, articles were included based on pre-determined criteria. The authors identified 32 relevant articles. Occupational exposure to wildland fires affects lung function in the short term and may increase the risk of hypertension in the long term. Exposure to wildland fires is also associated with post-traumatic stress symptoms. There was insufficient evidence to comment on most longer-term risks, and in particular on respiratory disease or cancer risks. Further research is required to understand whether occupational exposure to wildland fires results in clinically significant impacts on respiratory function, and to further clarify the relationship between occupational exposure and blood pressure, mental health, and cancer outcomes. Int J Occup Med Environ Health. 2019;32(2):121-40.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.080
Threshold uncertainty score0.791

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.104
GPT teacher head0.441
Teacher spread0.338 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
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

Citations85
Published2019
Admission routes1
Has abstractyes

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