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Exposure to Pesticides and Non-Hodgkin Lymphoma in Canadian Women

2007· article· en· W2437503085 on OpenAlexaffabout
Ji Hu, Marie DesMeules

Bibliographic record

VenueEpidemiology · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPesticide Exposure and Toxicity
Canadian institutionsPublic Health Agency of Canada
Fundersnot available
KeywordsMedicinePesticideOdds ratioEnvironmental healthLogistic regressionEtiologyPopulationCase-control studyInternal medicineBiology

Abstract

fetched live from OpenAlex

ISEE-103 Objective: Exposure to pesticides is recognized as an important environmental factor associated with increased risk of cancer. The study examines the association between exposure to pesticides and the risk of non-Hodgkin lymphoma (NHL) in Canadian women. Material and Methods: Mailed questionnaires were completed by 789 incidents, histologically confirmed cases of NHL, and 2492 population controls between 1994 and 1997 in 8 Canadian provinces. Measurement included information on socioeconomic status, lifestyle habits, diet, occupation, or nonoccupational exposure to pesticides and years of exposure. Odds ratios (ORs) and 95% confidence intervals (CIs) were derived through unconditional logistic regression. Results: Exposure to pesticides had an increased risk of NHL. Compared with no exposure to pesticides, the OR was 1.5 (95% CI, 1.1–2.0). ORs increased with increasing exposure in years to pesticides (OR, 1.2 for 1–3 years exposure and 1.5 for >3 years). It was notable that 65% Canadian women exposed to pesticides at home and 30% in both at home and at work. Only 5% women exposed to pesticides at work. Conclusions: Case-control and cohort studies have given particular attention to agricultural pesticide use and risk of NHL. Most studies were focused on men. A number of studies reported that occupational exposure to pesticides increased the risk of NHL. We found that nonoccupational exposure to pesticides may play a major role in the etiology of NHL in Canadian women. Our findings add to the evidence that exposure to pesticides increased the risk of 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)0.003
metaresearch head score (Gemma)0.002
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 score0.583
Threshold uncertainty score0.788

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
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 head0.024
GPT teacher head0.268
Teacher spread0.243 · 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

Citations0
Published2007
Admission routes2
Has abstractyes

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