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Record W1981077021 · doi:10.1002/ajim.20705

Under‐reporting of compensable mesothelioma in Alberta

2009· article· en· W1981077021 on OpenAlexaffabout
Marilyn Cree, M Lalji, Bei Jiang, Keumhee C. Carrière

Bibliographic record

VenueAmerican Journal of Industrial Medicine · 2009
Typearticle
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsUniversity of AlbertaAlberta Cancer Foundation
Fundersnot available
KeywordsMesotheliomaAsbestosMedicineWorkers' compensationCompensation (psychology)IndemnityEnvironmental healthCancer registryFamily medicineActuarial sciencePathologyPopulationBusiness

Abstract

fetched live from OpenAlex

BACKGROUND: When combined with a history of occupational asbestos exposure, mesothelioma is often presumed work-related. In Canada, workers diagnosed with mesothelioma caused by occupational asbestos exposure are often eligible for compensation under provincial workers' compensation boards. Although occupational asbestos exposure causes the majority of mesothelioma, Canadian research suggests less than half of workers actually apply for compensation. Alberta's mandatory reporting requirements may produce higher filing rates but this is currently unknown. This study evaluates Alberta's mesothelioma filing and compensation rates. METHODS: Demographic information on all mesothelioma patients diagnosed between 1980 and 2004 were extracted from the Alberta Cancer Board's Cancer Registry and linked to Workers' Compensation Board of Alberta claims data. RESULTS: Alberta recorded a total of 568 histologically confirmed mesothelioma cases between 1980 and 2004. Forty-two percent of cases filed a claim; 83% of filed claims were accepted for compensation. CONCLUSIONS: Patient under-reporting of compensable mesothelioma is a problem and raises larger questions regarding under-reporting of other asbestos-related cancers in Alberta. Strategies should focus on increasing filing rates where appropriate.

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)0.002
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 score0.298
Threshold uncertainty score0.301

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.047
GPT teacher head0.321
Teacher spread0.274 · 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

Citations20
Published2009
Admission routes2
Has abstractyes

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