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Agricultural Dust Exposure and Respiratory Symptoms Among California Farm Operators

2005· article· en· W2056765651 on OpenAlexaff
Marc B. Schenker, Jeffrey A. Farrar, Diane C. Mitchell, Rochelle S. Green, Steven J. Samuels, Robert Lawson, Stephen A. McCurdy

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicIndoor Air Quality and Microbial Exposure
Canadian institutionsLawson Foundation
FundersNational Institute for Occupational Safety and HealthNational Institute of Environmental Health SciencesCenters for Disease Control and Prevention
KeywordsWheezeChronic bronchitisAsthmaMedicineOdds ratioBronchitisEnvironmental healthRespiratory systemConfidence intervalChronic coughLogistic regressionRespiratory diseasePopulationRespiratory soundsInternal medicineLung

Abstract

fetched live from OpenAlex

OBJECTIVE: To study whether dust exposure in California agriculture is a risk factor for respiratory symptoms. METHODS: A population-based survey of 1947 California farmers collected respiratory symptoms, occupational and personal exposures. Associations between dust and respiratory symptoms were assessed by logistic regression models. RESULTS: The prevalence of persistent wheeze was 8.6%, chronic bronchitis 3.8%, chronic cough 4.2%, and asthma 7.8%. Persistent wheeze was independently associated with dust in a dose-response fashion odds ratio, 1.2 (95% confidence interval[CI]=0.8-2.0) and 1.8 (95% CI=1.1-3.2) for low and high time in dust. A borderline significant association between chronic bronchitis and dust exposure was found. Asthma was associated with keeping livestock, but not with dust exposure. CONCLUSIONS: Occupational dust exposure among California farmers, only one third of whom tended animals, was independently associated with chronic respiratory symptoms.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score1.000

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 studies0.0000.001
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 head0.011
GPT teacher head0.231
Teacher spread0.220 · 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.

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

Citations57
Published2005
Admission routes1
Has abstractyes

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