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Record W7095710804

# 2005 British Occupational Hygiene Society Published by Oxford University Press doi:10.1093/annhyg/mei001 Mortality from Lung and Kidney Disease in a Cohort of North American Industrial Sand Workers: An Update

2005· article· en· W7095710804 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsnot available
Fundersnot available
KeywordsSilicosisCohortKidney diseaseCause of deathLung cancerDiseaseCohort studyMortality rate
DOInot available

Abstract

fetched live from OpenAlex

tionship between quartz exposure and death from both silicosis and lung cancer, after allowance for cigarette smoking and in the absence of known occupational carcinogens. Unexpectedly, a significant excess mortality from chronic non-malignant renal disease [observed 16; expected 7.6; standardized mortality ratio (SMR) 212] was also found, whereas deaths from renal cancer at this stage were close to expectation (observed 6; expected 5.2). Objectives: Our primary aim was to discover whether death from chronic renal disease was related to the estimated intensity of crystalline silica exposure. A further aim was to determine whether or not our previous estimates of lung cancer and silicosis risk were confirmed by mortality in the cohort 6 years later. Methods: With help from the US National Death Index, surviving members of the cohort, with the exception of employees of a small plant in Canada, were traced through 2000. The cause of death was determined for all who had died, for comparison against National and State mortality rates. Nested case-referent analyses were then undertaken, as previously, of deaths from lung cancer and silicosis, plus end-stage renal disease and kidney cancer, in relation to

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.105
Threshold uncertainty score0.350

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.1050.039

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 head0.012
GPT teacher head0.239
Teacher spread0.227 · 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 source (direct Gemma or distilled Codex), 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
Published2005
Admission routes1
Has abstractyes

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