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Record W4393162479 · doi:10.23866/brnrev:2024-m0103

Air pollution and lung cancer

2024· article· en· W4393162479 on OpenAlexaff
Stephen Lam, Michelle C. Turner

Bibliographic record

VenueBarcelona Respiratory Network · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLung cancerAir pollutionPollutionEnvironmental scienceMedicineEnvironmental healthEnvironmental planningOncologyChemistryBiology

Abstract

fetched live from OpenAlex

Outdoor air pollution and particulate matter (PM) in outdoor air is a major cause of lung cancer. The purpose of this article is to review the current knowledge regarding the effects of outdoor air pollution on lung cancer development and progression. There is clear and substantial evidence of a link between outdoor ambient air pollution and lung cancer. There is an interplay between environmental exposure and host factors, especially in those with genetic risk. PM can promote development and progression of lung cancer through inflammatory and immune mechanisms on pre-existing oncogenic mutations, by altering the lung microbiome and through metabolic perturbations. There is an urgent need to support efforts to improve air quality by reducing fossil fuel use. Health professionals and policy decision makers play an important role in supporting continued advances in research and implementation of measures to reduce the adverse health effects of outdoor air pollution.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

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

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 head0.028
GPT teacher head0.318
Teacher spread0.290 · 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 designNot applicable
Domainnot available
GenreEmpirical

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

Citations1
Published2024
Admission routes1
Has abstractyes

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