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Record W2137872756 · doi:10.1093/occmed/kqi124

Mortality and cancer incidence in Ontario glass fiber workers

2005· article· en· W2137872756 on OpenAlexaffabout
Harry S. Shannon, Angus Muir, Ted Haines, Dave K. Verma

Bibliographic record

VenueOccupational Medicine · 2005
Typearticle
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineLung cancerIncidence (geometry)Glass woolCohort studyProspective cohort studyCancer incidenceOccupational medicineCancerEpidemiologyCohortStandardized mortality ratioDemographySurgeryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: In a previous cohort study of glass fiber manufacturing, we found a significant increase in lung cancer. This study extends the follow-up period. METHODS: We conducted a historical prospective study of 2557 men employed in producing glass wool. We obtained work histories, causes and dates of death, and date and site of cancer diagnoses. We computed standardized mortality and incidence ratios (SMR, SIR). RESULTS: The overall SMR for lung cancer was 163 (95% CI = 118-221). The SMR did not vary consistently by duration of employment and time since first employment. However, plant workers with >20 years' employment and >40 years since first exposure had an SMR for lung cancer of 282 (95% CI = 113-582). The SMR dropped with later date of first exposure, but the trend was non-significant. There was an unexpected overall increase in kidney cancer incidence. DISCUSSION: The increase in lung cancer is greater than in other cohorts of glass fiber workers. Since exposure data are lacking from the early years of the plant, we cannot state if the excess was due to glass fibers, other work exposures or other reasons.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score0.244

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.043
GPT teacher head0.336
Teacher spread0.292 · 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

Citations42
Published2005
Admission routes2
Has abstractyes

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