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Silica, silicosis, and lung cancer: a risk assessment

2000· article· en· W2023668967 on OpenAlexaff
Murray M. Finkelstein

Bibliographic record

VenueAmerican Journal of Industrial Medicine · 2000
Typearticle
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineSilicosisLung cancerOccupational exposureRisk assessmentCancerPneumoconiosisEnvironmental healthOncologyInternal medicinePathology

Abstract

fetched live from OpenAlex

BACKGROUND: To investigate exposure-response relationships for silica, silicosis, and lung cancer. METHODS: Quantitative review of the literature identified in a computerized literature search. RESULTS: The risk of silicosis (ILO category 1/1 or more) following a lifetime of exposure at the current OSHA standard of 0.1 mg/m(3) is likely to be at least 5-10% and lung cancer risk is likely to be increased by 30% or more. The exposure-response relation for silicosis is nonlinear and reduction of dust exposures would have a greater than linear benefit in terms of risk reduction. Available data suggests that 30 years exposure at 0.1 mg/m(3) might lead to a lifetime silicosis risk of about 25%, whereas reduction of the exposure to 0.05 mg/m(3) might reduce the risk to under 5%. CONCLUSIONS: The lifetime risk of silicosis and lung cancer at an exposure level of 0.1 mg/m(3) is high. Lowering exposures to the NIOSH recommended limit if 0.05 mg/m(3) may have substantial benefit.

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 score0.488
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0020.000

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 head0.013
GPT teacher head0.314
Teacher spread0.301 · 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

Citations70
Published2000
Admission routes1
Has abstractyes

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