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Record W3018276128 · doi:10.1164/rccm.201910-1926oc

Respirable Crystalline Silica Exposure, Smoking, and Lung Cancer Subtype Risks. A Pooled Analysis of Case–Control Studies

2020· article· en· W3018276128 on OpenAlexafffundabout
Calvin Ge, Susan Peters, Ann Olsson, Lützen Portengen, Joachim Schüz, Josué Almansa, Thomas Behrens, Beate Pesch, Benjamin Kendzia, Wolfgang Ahrens, Vladimír Bencko, Simone Benhamou, Paolo Boffetta, Bas Bueno‐de‐Mesquita, Neil E. Caporaso, Dario Consonni, Paul A. Demers, Eleonóra Fabiánová, Guillermo Fernández‐Tardón, John K. Field, Francesco Forastiere, Lenka Foretová, Pascal Guénel, Per Gustavsson, Vikki Ho, Vladimí­r Janout, Karl‐Heinz Jöckel, Stefan Karrasch, Maria Teresa Landi, Jolanta Lissowska, Danièle Luce, Dana Mateș, Franco Merletti, Dario Mirabelli, Nils Plato, Hermann Pohlabeln, Lorenzo Richiardi, Péter Rudnai, Jack Siemiatycki, Beata Świątkowska, Adonina Tardón, H.-Erich Wichmann, David Zaridze, Thomas Brüning, Kurt Straíf, Hans Kromhout, Roel Vermeulen

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsPublic Health OntarioUniversity of TorontoUniversité de MontréalOccupational Cancer Research CentreCancer Care Ontario
FundersFonds de Recherche du Québec - SantéUniversiteit UtrechtMinistère de l'Économie, de la Science et de l'Innovation - QuébecDeutsche Gesetzliche UnfallversicherungCancer Research SocietyWorld Health Organization
KeywordsMedicineLung cancerOdds ratioConfidence intervalCancerAdenocarcinomaCase-control studyInternal medicineLogistic regressionEnvironmental healthOncology

Abstract

fetched live from OpenAlex

Abstract Rationale Millions of workers around the world are exposed to respirable crystalline silica. Although silica is a confirmed human lung carcinogen, little is known regarding the cancer risks associated with low levels of exposure and risks by cancer subtype. However, little is known regarding the disease risks associated with low levels of exposure and risks by cancer subtype. Objectives We aimed to address current knowledge gaps in lung cancer risks associated with low levels of occupational silica exposure and the joint effects of smoking and silica exposure on lung cancer risks. Methods Subjects from 14 case–control studies from Europe and Canada with detailed smoking and occupational histories were pooled. A quantitative job-exposure matrix was used to estimate silica exposure by occupation, time period, and geographical region. Logistic regression models were used to estimate exposure–disease associations and the joint effects of silica exposure and smoking on risk of lung cancer. Stratified analyses by smoking history and cancer subtypes were also performed. Measurements and Main Results Our study included 16,901 cases and 20,965 control subjects. Lung cancer odds ratios ranged from 1.15 (95% confidence interval, 1.04–1.27) to 1.45 (95% confidence interval, 1.31–1.60) for groups with the lowest and highest cumulative exposure, respectively. Increasing cumulative silica exposure was associated (P trend < 0.01) with increasing lung cancer risks in nonsilicotics and in current, former, and never-smokers. Increasing exposure was also associated (P trend ≤ 0.01) with increasing risks of lung adenocarcinoma, squamous cell carcinoma, and small cell carcinoma. Supermultiplicative interaction of silica exposure and smoking was observed on overall lung cancer risks; superadditive effects were observed in risks of lung cancer and all three included subtypes. Conclusions Silica exposure is associated with lung cancer at low exposure levels. An exposure–response relationship was robust and present regardless of smoking, silicosis status, and cancer subtype.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.475

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.035
GPT teacher head0.361
Teacher spread0.327 · 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 designObservational
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

Citations90
Published2020
Admission routes3
Has abstractyes

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