Farm Characteristics, Allergy Symptoms, and Risk of Non-Hodgkin Lymphoid Neoplasms in the Agricultural Health Study
Bibliographic record
Abstract
BACKGROUND: Because of unique exposures, studies among farmers may yield insights into the relationship between allergies and non-Hodgkin lymphoid (NHL) neoplasms. We evaluated farm characteristics, allergic symptoms and conditions, and risk of NHL including specific subtypes in the Agricultural Health Study, a prospective cohort of farmers and spouses from North Carolina and Iowa. METHODS: We identified 710 incident cases of NHL (including chronic lymphocytic leukemia and multiple myeloma) among 82,370 participants with baseline data on crop and animal exposures, including 454 cases among 52,850 participants with baseline data on recent allergy symptoms (rhinitis) and living on a farm during childhood. HR and 95% confidence intervals (CI) were calculated using multivariable-adjusted proportional hazards models. RESULTS: We observed reduced risks of NHL among farmers and spouses with rhinitis at baseline (HR, 0.63; 95% CI, 0.51-0.79), related to growing soybeans (HR, 0.80; 95% CI, 0.67-0.96), and among farmers who handled stored grains or hay (HR, 0.66; 95% CI, 0.52-0.82). Growing up on a farm was associated with increased NHL risk (HR, 1.51; 95% CI, 1.15-1.98). Results did not differ significantly by NHL subtype. CONCLUSIONS: Both the reduced risk of NHL among those with allergy symptoms and specific farm exposures in adulthood, and the increased risk among those who grew up on a farm suggest that the host immune response to agricultural allergens may influence NHL development. IMPACT: This prospective study is, to our knowledge, the first to investigate the relationship between allergy symptoms and NHL risk in farmers; confirmation of these findings in other farming populations is warranted. Cancer Epidemiol Biomarkers Prev; 24(3); 587-94. ©2015 AACR.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".