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Record W2095515430 · doi:10.1097/cej.0b013e328345f971

Chlorinated pesticides and cancer of the head and neck

2011· article· en· W2095515430 on OpenAlexaff
Gregg Govett, Stephen J. Genuis, Hannah E. Govett, Sanjay Beesoon

Bibliographic record

VenueEuropean Journal of Cancer Prevention · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCarcinogens and Genotoxicity Assessment
Canadian institutionsUniversity of Alberta
FundersCenters for Disease Control and PreventionHealth Research BoardOklahoma State UniversityU.S. Environmental Protection Agency
KeywordsBioaccumulationMedicineHead and neckAdipose tissueHead and neck cancerCancerPesticideCarcinogenCohortPopulationAlcohol consumptionEnvironmental healthPhysiologyOncologyInternal medicineAlcoholSurgeryBiologyChemistryEnvironmental chemistryGenetics

Abstract

fetched live from OpenAlex

Cancer of the head and neck is a pervasive problem with recognized determinants including tobacco use, alcohol consumption, and earlier radiation exposure. Organochlorine pesticides (OCPs) have been shown to have carcinogenic potential in both animals and humans. OCPs have previously been widely used in the agricultural industry of rural Oklahoma. Seven patients from rural Oklahoma with head and neck cancer and without any of the usual risk factors were tested for the presence of OCPs in their adipose tissue. Clinical and toxicological data on each of these patients are presented for consideration. Results were compared with (i) levels from five individuals not experiencing cancer but who lived in the same area, and (ii) adipose tissue OCP levels in other population groups. Each of the seven patients tested had markedly elevated levels of some OCPs in their adipose tissue compared with the cohort of noncancer patients. Further research is required to confirm whether there is a causative link between OCP bioaccumulation and head and neck cancer as suggested by this case series.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.677
Threshold uncertainty score0.151

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)0.0000.000

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 head0.025
GPT teacher head0.287
Teacher spread0.261 · 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 designBench or experimental
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

Citations9
Published2011
Admission routes1
Has abstractyes

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