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Record W2107937980 · doi:10.1093/annhyg/men036

Asbestos Fibre Concentrations in the Lungs of Brake Workers: Another Look

2008· article· en· W2107937980 on OpenAlexaffabout
Murray M. Finkelstein

Bibliographic record

VenueThe Annals of Occupational Hygiene · 2008
Typearticle
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsMount Sinai Hospital
Fundersnot available
KeywordsChrysotileAsbestosTremoliteBrakeMesotheliomaEnvironmental scienceMedicineEnvironmental healthToxicologyPathologyEngineeringComposite materialMaterials scienceBiologyAutomotive engineering

Abstract

fetched live from OpenAlex

OBJECTIVE: To reanalyse data on the lung content of asbestos fibres among brake mechanics. METHODS: I re-analysed data published by Butnor, Roggli and colleagues on the lung content of chrysotile and tremolite asbestos fibres among brake mechanics and controls. Statistics of the distributions were estimated by maximum likelihood to accommodate observations below the detection limit. Mean concentrations were compared by the t-test, bootstrap resampling and interval-censored survival methods. RESULTS: The mean concentrations of fibres were higher among the brake workers than the controls. The concentration of tremolite fibres was higher than the concentration of chrysotile, a pattern similar to that observed among Quebec chrysotile miners and millers. CONCLUSIONS: Re-analysis of published data does not support the interpretation that, in automotive brake repair workers with malignant mesothelioma, asbestos content is within the normal range. The alternative interpretation that brake mechanics have a greater than background burden of asbestos fibres, attributable to occupational exposure to dusts from friction products manufactured from Canadian chrysotile, appears more credible. This asbestos burden might be associated with an increased risk of asbestos-associated cancers.

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 score0.009
Threshold uncertainty score0.366

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 studies0.0000.000
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 head0.097
GPT teacher head0.345
Teacher spread0.248 · 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

Citations34
Published2008
Admission routes2
Has abstractyes

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