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Record W4246911714 · doi:10.1136/oemed-2013-101717.103

103 Interactions between occupational exposures to extremely low frequency magnetic field and chemicals for brain tumour risk in the INTEROCC study

2013· article· en· W4246911714 on OpenAlexaffabout
Charles H. Turner, Benke, Bowman, Figuerola-Alquezar, Fleming Fleming, Hours, Kincl, Krewski, Lavoué, McLean, Parent, Richardson, Sadetzki, Schlaefer, Schlehofer, Siemiatycki, Van Tongeren, Cardis

Bibliographic record

VenueOccupational and Environmental Medicine · 2013
Typearticle
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsInstitut National de la Recherche ScientifiqueArmand Frappier MuseumUniversité de MontréalUniversity of Ottawa
Fundersnot available
KeywordsMedicineGliomaOdds ratioEpidemiologyEnvironmental healthOccupational exposurePathologyOncologyCancer research

Abstract

fetched live from OpenAlex

<h3>Objectives</h3> Brain tumors are a serious, often highly disease with few established risk factors. Although ionizing radiation has been clearly linked with brain tumors, there are a number of other environmental and occupational agents suspected. There may also be interactions between occupational agents for brain tumors however the epidemiological literature is sparse. Only one previous epidemiological study examined potential interactive effects between occupational exposure to extremely low frequency magnetic fields (ELF-MF) and chemical agents with various interactive effects observed. The objective of this paper was to examine the possible joint effects of occupational agents for brain tumors (specifically glioma and meningioma) including occupational ELF-MF and chemicals in the large-scale INTEROCC study. <h3>Methods</h3> The INTEROCC study is formed by seven participating countries (Australia, Canada, France, Germany, Israel, New Zealand, United Kingdom) from the parent INTERPHONE study. Cases of primary brain glioma and meningioma aged at least 20 years were recruited between 2000 and 2004. Detailed occupational history data was collected for jobs held at least six months. Job titles were coded into standard international occupational classifications and estimates of ELF-MF and chemical exposure were assigned based on job exposure matrices. Odds ratios (and 95% confidence intervals) for single and joint occupational exposures were calculated according to a common reference category. Interactions on both the additive and multiplicative scale were assessed. <h3>Results</h3> Data on a total of 3,978 brain tumor cases, including 2,054 gliomas and 1,924 meningiomas, were analysed with 5,601 control subjects. A number of interactions were observed, varying according to exposure time window, exposure metric, and included subjects. Results also varied according to tumour type. <h3>Conclusion</h3> Interactions between occupational agents for brain tumors were observed however further research examining possible joint effects of occupational agents for brain tumours with refined assessments of occupational exposure in other large-scale studies is warranted.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.712

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.021
GPT teacher head0.299
Teacher spread0.279 · 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

Citations1
Published2013
Admission routes2
Has abstractyes

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