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Record W4400485995 · doi:10.1016/j.lungcan.2024.107861

Asbestos-Related lung Cancer: An underappreciated oncological issue

2024· review· en· W4400485995 on OpenAlexaboutno aff
Nico van Zandwijk, Arthur L. Frank, Glen Reid, Oluf Dimitri Røe, Christopher I. Amos

Bibliographic record

VenueLung Cancer · 2024
Typereview
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsnot available
FundersUniversity of SydneySydney Local Health District
KeywordsAsbestosLung cancerMesotheliomaMedicineAsbestosisIncidence (geometry)Environmental healthCancerOncologyPathologyInternal medicineLung

Abstract

fetched live from OpenAlex

Asbestos, a group of class I (WHO) carcinogenic fibers, is the main cause of mesothelioma. Asbestos inhalation also increases the risk to develop other solid tumours with lung cancer as the most prominent example [91Zona A. Fazzo L. Benedetti M. Bruno C. Vecchi S. Pasetto R. Di Fonzo D. SENTIERI-Epidemiological study of residents in National Priority Contaminated Sites. Sixth report.Epidemiol. Prev. 2023; 47: 1-286https://doi.org/10.19191/EP23.1-2-S1.003Crossref PubMed Scopus (0) Google Scholar]. The incidence of asbestos-related lung cancer (ARLC) is estimated to be to six times larger than the mesothelioma incidence thereby becoming an important health issue [86Villeneuve P.J. Parent M.E. Harris S.A. Johnson K.C. Canadian Canc R. Occupational exposure to asbestos and lung cancer in men: evidence from a population-based case-control study in eight Canadian provinces.BMC Cancer. 2012; 12595https://doi.org/10.1186/1471-2407-12-595Crossref PubMed Scopus (33) Google Scholar]. Although the pivotal role of asbestos in inducing lung cancer is well established, the precise causal relationships between exposures to asbestos, tobacco smoke, radon and ‘particulate’ (PM2.5) air pollution remain obscure and new knowledge is needed to establish appropriate preventive measures and to tailor existing screening practices[22Finnish Institute of Occupational Health. (2014). Asbestos, asbestosis, and cancer: Helsinki criteria for diagnosis and attribution 2014. https://www.ttl.fi/sites/default/files/2023-04/asbestos-asbestosis-and-cancer-book.pdf.Google Scholar, 61Raffn E. Lynge E. Juel K. Korsgaard B. Incidence of cancer and mortality among employees in the asbestos cement industry in Denmark.Occupational and Environmental Medicine. 1989; 46: 90-96https://doi.org/10.1136/oem.46.2.90Crossref Scopus (100) Google Scholar, 65Røe O.D. Stella G.M. Malignant pleural mesothelioma: history, controversy, and future of a man-made epidemic.Asbestos and Mesothelioma. 2017; 73–101https://doi.org/10.1183/09059180.00007014Crossref Scopus (137) Google Scholar]. We hypothesize that a part of the increasing numbers of lung cancer diagnoses in never-smokers can be explained by (historic and current) exposures to asbestos as well as combinations of different forms of air pollution (PM2.5, asbestos and silica).

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.860
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.039
GPT teacher head0.430
Teacher spread0.391 · 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
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

Citations15
Published2024
Admission routes1
Has abstractyes

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