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Record W2003049560 · doi:10.1002/ajim.22063

Malignant mesothelioma incidence among talc miners and millers in New York State

2012· article· en· W2003049560 on OpenAlexaff
Murray M. Finkelstein

Bibliographic record

VenueAmerican Journal of Industrial Medicine · 2012
Typearticle
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsTremoliteMesotheliomaAsbestosMedicineTalcIncidence (geometry)PopulationCohortEnvironmental healthSurgeryDemographyPathologyBiology

Abstract

fetched live from OpenAlex

BACKGROUND: There is controversy about the potential for dust from the talc mines and mills of New York State to cause mesothelioma. Honda et al. published a study of mortality among New York talc workers and concluded that it was unlikely that the two deaths from mesothelioma were caused by talc ore dust. However, fibers of tremolite and anthophyllite have been found in the lungs of talc workers and Hull concluded that "New York talc exposure is associated with mesothelioma, and deserves further public health attention." METHODS: Data concerning additional cases of mesothelioma in the cohort have been posted by NIOSH. I used information from the NIOSH website and the Honda report to analyze the incidence of mesothelioma during the years 1990-2007. RESULTS: There were at least five new cases of mesothelioma in the cohort and mesothelioma incidence rates were at least five (1.6-11.7) times the rate in the general population (P < 0.01). CONCLUSIONS: I conclude that: (1) mesothelioma has been diagnosed among members of the cohort at a rate in excess of that in the general population; (2) fibers of tremolite and anthophyllite have been detected in dust and the lungs of talc workers; and (3) these fibers are known causes of mesothelioma. It is prudent, on the balance of probabilities, to conclude that dusts from New York State talc ores are capable of causing mesothelioma in exposed individuals.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.403

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.034
GPT teacher head0.275
Teacher spread0.240 · 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

Citations30
Published2012
Admission routes1
Has abstractyes

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