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Record W2013229228 · doi:10.1002/ajim.20753

Data linkage to estimate the extent and distribution of occupational disease: new onset adult asthma in Alberta, Canada

2009· article· en· W2013229228 on OpenAlexafffundabout
Nicola Cherry, Jeremy Beach, Igor Burstyn, Xiangning Fan, Na Guo, Nitin Kapur

Bibliographic record

VenueAmerican Journal of Industrial Medicine · 2009
Typearticle
Languageen
FieldMedicine
TopicOccupational exposure and asthma
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health ResearchGovernment of Alberta
KeywordsMedicineAsthmaOccupational asthmaIncidence (geometry)Job-exposure matrixWorkers' compensationEnvironmental healthOccupational medicineDemographyOccupational exposureCompensation (psychology)Internal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Although occupational asthma is a well recognized and preventable disease, the numbers of cases presenting for compensation may be far lower than the true incidence. METHODS: Workers' Compensation Board (WCB) claims for any reason 1995-2004 were linked to physician billing data. New onset adult asthma (NOAA) was defined as a billing for asthma (ICD-9 code of 493) in the 12 months prior to a WCB claim without asthma in the previous 3 years. Incidence was calculated by occupation, industry and, in a case-referent analysis, exposures estimated from an asthma specific job exposure matrix. RESULTS: There were 782,908 WCB eligible claims, with an incidence rate for NOAA of 1.6%: 23 occupations and 21 industries had a significantly increased risk. Isocyanates (OR 1.54: 95% CI 1.01-2.36) and exposure to mixed agricultural allergens (OR = 1.59: 95% CI 1.17-2.18) were related to NOAA overall, as were exposures to cleaning chemicals in men (OR = 1.91:95% CI 1.34-2.73). Estimates of the number of cases of occupational asthma suggested a range of 4% to about half for the proportion compensated. CONCLUSIONS: Data linkage of administrative records can demonstrate under-reporting of occupational asthma and indicate areas for prevention.

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)0.004
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 score0.636
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
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 head0.032
GPT teacher head0.342
Teacher spread0.310 · 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

Citations48
Published2009
Admission routes3
Has abstractyes

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