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Record W2326347011 · doi:10.1158/1538-7445.am2011-1877

Abstract 1877: Lung cancer risk in painters: Results from the SYNERGY pooled analysis

2011· article· en· W2326347011 on OpenAlexaffabout
Neela Guha, Ann Olsson, Thomas Brüning, Beate Pesch, Benjamin Kendzia, Heinz‐Erich Wichmann, Irene Brüske, Dario Consonni, Maria Teresa Landi, Neil E. Caporaso, Jack Siemiatycki, Per Gustavsson, Nils Plato, Franco Merletti, Dario Mirabelli, Lorenzo Richiardi, Wolfgang Ahrens, Hermann Pohlabeln, Karl‐Heinz Jöckel, David Zaridze, Adrian Cassidy, Jolanta Lissowska, Neonila Szeszenia‐Dąbrowska, Isabelle Stücker, Simone Benhamou, Vladimír Bencko, Lenka Foretová, Vladimí­r Janout, Péter Rudnai, Eleonóra Fabiánová, Rodica Stanescu Dumitru, Francesco Forastiere, Bas Bueno‐de‐Mesquita, Isabelle Groß, Véronique Benhaı̈m-Luzon, Susan Peters, Roel Vermeulen, Paolo Boffetta, Hans Kromhout, Kurt Straíf

Bibliographic record

VenueCancer Research · 2011
Typearticle
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsInternational agencyLung cancerAsbestosMedicineOdds ratioCarcinogenCancerEnvironmental healthLogistic regressionMesotheliomaInternal medicineDemographyPathologyBiology

Abstract

fetched live from OpenAlex

Abstract “Occupational exposure as a painter” was classified as carcinogenic to humans by the International Agency for Research on Cancer (IARC) based on increased risks of cancers of the lung, bladder, and mesothelioma. Painters are exposed to a mixture of known and suspected lung carcinogens; thus it has been difficult to identify the specific agent(s) contributing to the elevated risk of lung cancer. Although the exposure to occupational carcinogens could differ according to the job duties of a painter, it is unknown whether the risk of lung cancer differs according to the painter type. Data from the SYNERGY study were used to evaluate the risk of lung cancer associated with ever working as a painter, duration of employment, and type of painter (classified according to ISCO and ISIC codes). SYNERGY is a pooled effort of 11 case-control studies in European countries and Canada that includes detailed individual data on smoking for 13389 lung cancer cases and 16384 age- and sex-matched controls. Among the cases and controls, there were 462 and 383 painters, respectively. Multivariable unconditional logistic regression models were adjusted for age, gender, centre, tobacco pack-years, and occupational exposures to asbestos, silica, polycyclic aromatic hydrocarbons, chromium VI and nickel as assessed by a job-exposure matrix. An odds ratio (OR) of 1.31 (95% CI, 1.12-1.45) was associated with ever working as painter and the risk of lung cancer increased with increasing years of employment (p-value for trend = 0.0004). A similar magnitude of effect and trend with duration of employment was observed in construction painters (ISCO 93120/ISIC 5000) but not in automobile painters (ISCO 93960/ISIC 3843,9513). Results were similar when restricted to men but uninformative for women only due to small numbers. There was no significant difference in risk when stratified by histological type and restricted to never smokers. Painters, particularly in the construction industry, are at an increased risk for lung cancer and this risk increases with duration of employment. These results will be further refined by specific type of painter and by including additional studies to increase the precision of the risk estimates. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 1877. doi:10.1158/1538-7445.AM2011-1877

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.019
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.027
Bibliometrics0.0050.004
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.064
GPT teacher head0.380
Teacher spread0.316 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
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

Citations0
Published2011
Admission routes2
Has abstractyes

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