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Record W2097477476 · doi:10.1002/ajim.10041

Canadian male farm residents, pesticide safety handling practices, exposure to animals and non-Hodgkin's lymphoma (NHL)

2002· article· en· W2097477476 on OpenAlexaffabout
Helen H. McDuffie, Punam Pahwa, John J. Spinelli, John McLaughlin, S Fincham, Deborah Robson, J A Dosman, Jia Hu

Bibliographic record

VenueAmerican Journal of Industrial Medicine · 2002
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPesticide Exposure and Toxicity
Canadian institutionsAlberta Health ServicesUniversity of TorontoBC Cancer AgencySaskatchewan Cancer AgencyLunenfeld-Tanenbaum Research InstituteUniversity of SaskatchewanRoyal University Hospital
Fundersnot available
KeywordsMedicineOdds ratioEnvironmental healthPersonal protective equipmentPopulationConfidence intervalNon-Hodgkin's lymphomaLymphomaInternal medicineDisease

Abstract

fetched live from OpenAlex

BACKGROUND: A majority of published studies indicate that farmers have an elevated risk of developing non-Hodgkin's lymphoma (NHL) compared to other workers. METHODS: We evaluated accidental exposure to pesticides, the use of personal protective equipment, and exposure to animals among male farm residents in a Canadian population-based, multi-centre, NHL-control questionnaire study. RESULTS: In a multivariate model, the following variables had statistically significant adjusted odds ratios (OR) using 95% confidence intervals (95% CI) (a) higher risk: having more than 13 head of swine, raising bison, elk or ostriches, a personal history of cancer, > 4 and < or = 15 years of farm residence and occupational exposure to diesel fuel and exhaust; (b) lower risk: raising cattle and a personal history of measles. CONCLUSIONS: Future multidisciplinary studies of NHL should include a comprehensive review of exposure to animals in sufficient detail to assess etiological mechanisms to explain the putative associations between exposure to farm animals and NHL.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.840
Threshold uncertainty score0.959

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.039
GPT teacher head0.258
Teacher spread0.218 · 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

Citations44
Published2002
Admission routes2
Has abstractyes

Explore more

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