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Silica, Silicosis, and Lung Cancer: A Response to a Recent Working Group Report

2000· review· en· W2152398108 on OpenAlexaff
Patrick A. Hessel, John F. Gamble, J. Bernard L. Gee, Graham W. Gibbs, Francis H. Y. Green, W. K. C. Morgan, Brooke T. Mossman

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2000
Typereview
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSilicosisCarcinogenLung cancerCancerMedicineCarcinogenesisOccupational exposureEnvironmental healthPathologyInternal medicineChemistryBiochemistry

Abstract

fetched live from OpenAlex

The relationship between crystalline silica and lung cancer has been the subject of many recent publications, conferences, and regulatory considerations. An influential, international body has determined that there was sufficient evidence to conclude that quartz and cristobalite are carcinogenic in humans. The present authors believe that the results of these studies are inconsistent and, when positive, only weakly positive. Other, methodologically strong, negative studies have not been considered, and several studies viewed as providing evidence supporting the carcinogenicity of silica have significant methodological weaknesses. Silica is not directly genotoxic and is a pulmonary carcinogen only in the rat, a species that seems to be inappropriate for assessing particulate carcinogenesis in humans. Data on humans demonstrate a lack of association between lung cancer and exposure to crystalline silica. Exposure-response relationships have generally not been found. Studies in which silicotic patients were not identified from compensation registries and in which enumeration was complete did not support a causal association between silicosis and lung cancer, which further argues against the carcinogenicity of crystalline silica.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.966
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.042
GPT teacher head0.363
Teacher spread0.321 · 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 designOther design
Domainnot available
GenreReview

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

Citations79
Published2000
Admission routes1
Has abstractyes

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