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Record W2325598043 · doi:10.1158/1538-7445.am2011-1875

Abstract 1875: Lung cancer risk among hairdressers in SYNERGY – pooled analysis from case-control studies in Europe and Canada with detailed smoking data

2011· article· en· W2325598043 on OpenAlexaffabout
Ann Olsson, Neela Guha, 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, Roel Vermeulen, Paolo Boffetta, Hans Kromhout, Kurt Straíf

Bibliographic record

VenueCancer Research · 2011
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Risk Factors
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMedicineLung cancerOdds ratioInternational agencyDemographyConfidence intervalPopulationCohortCancer registryLogistic regressionCancerCohort studyTobacco controlEnvironmental healthInternal medicinePublic healthPathology

Abstract

fetched live from OpenAlex

Abstract “Occupational exposure as a hairdresser or barber” was classified as probably carcinogenic to humans by the International Agency for Research on Cancer in volume 99 (2010). Small increases in lung cancer risk (20-40%) are found in most cohort studies but without adequate adjustment for smoking. Studies in Scandinavia and the USA show a higher prevalence of smokers among hairdressers than in the general population. The SYNERGY project has pooled information on lifetime work histories (ISCO-68) and tobacco smoking from 13479 cases and 16350 controls, including 20% women, from 11 case-control studies in 12 European countries and Canada. The original studies were conducted between 1985 and 2005. Odds ratios (OR) for lung cancer and 95% confidence intervals (CI) were estimated by unconditional logistic regression, adjusted for age, sex, study, cigarette pack-years and time since quitting smoking. Less than 1% of the study population had ever worked as hairdresser or barber (0.89% of cases and 0.74% of controls). Hairdressers and barbers experienced a slight increase in lung cancer risk OR 1.16 (95%CI 0.90-1.49), which disappeared after adjusting for smoking OR 0.97 (95%CI 0.73-1.30). Results by duration of employment showed highest risks in hairdressers with short employment. Results were similar by gender, histology of lung cancer, and for women hairdressers. We observed a slight and non-significant increase in risk for male barbers, particularly in barbers with the longest employment and after adjustment for smoking. We could not detect an association between having worked as hairdresser or barber and increased lung cancer risk overall after adjusting for smoking. However, among male barbers we observed an increasing risk with increasing duration, although non-significant, after adjusting for tobacco smoking. Final results will include several more studies, and thereby increase the precision of our effect 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 1875. doi:10.1158/1538-7445.AM2011-1875

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)0.007
metaresearch head score (Gemma)0.010
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.457
Threshold uncertainty score0.920

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.010
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.013
Bibliometrics0.0060.007
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0020.001
Research integrity0.0010.001
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 head0.089
GPT teacher head0.386
Teacher spread0.298 · 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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