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Lung-Retained Asbestos Fibre Analysis: Trends Over 25 Years in One Institution and Their Implications

2006· article· en· W2082730576 on OpenAlexaff
Bruce W. Case, A. Dufresne, P Sébastien

Bibliographic record

VenueEpidemiology · 2006
Typearticle
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsMcGill University
Fundersnot available
KeywordsAsbestosMesotheliomaAsbestosisMedicinePopulationLung cancerCohortPathologyEnvironmental healthInternal medicineLung

Abstract

fetched live from OpenAlex

SAB2-O-07 Introduction: Lung-retained asbestos, as light-microscopic asbestos “bodies” (AB) and as electron microscope ascertained asbestos fiber (AF) analysis, was the first exposure biomarker. It began in the 1950s with counts of AB and reached maximum use in the 1990s with AF used in cohort and case-control studies. Methods: A 25-year experience in one university setting, using analyses of AB and AF representing general populations; environmental, domestic, and occupational exposures; “sentinel” animal data; and exposure assessment in epidemiologic cohorts and case-referent studies of mesothelioma, was assessed. Asbestos lung content was by 2006 evaluated in several thousand individuals on 4 continents. Methods were identical over time and results were available for all categories of AF length, width, aspect ratio, and chemistry as well as AB quantities. Time trends were assessed to determine important uses for asbestos-related exposure and disease. Results: The following trends were identified: Lung AF, and to a lesser extent AB, are declining over time. We find this to be true not only in the general population and to populations of individuals with the 3 common asbestos-related diseases (lung cancer, mesothelioma, and asbestosis). The latter finding may make the procedure less useful in future determinations of risk attribution and compensation hearings. The findings also suggest increased clearance of all fiber types. This may explain recent reports of past overestimation of future mesothelioma risk, especially in the Netherlands, Norway, Sweden, and Australia. AF and AB have consistently outperformed other indices of exposure as indicators of risk and radiologic change. This was especially true in application of our data to workers at a vermiculite mine in Libby, Montana, where we find that even asbestos bodies in sputum predict radiologic extent of asbestosis more accurately than do historical estimates of cumulative exposure derived from work histories. AF and AB analysis have identified environmental risk where it was missed by conventional air measurements. One example is our identification of high concentrations of tremolite asbestos in animals near natural asbestos deposits in the Western Slope of the Sierra Mountains in California. In North America, there has been a shift over time from crocidolite to amosite as the principal fiber associated with mesothelioma risk. Tremolite from natural sources has become increasingly important over all continents. Conclusions and Discussion: The results illustrate both the past and the future of biologic markers of exposure. Trends suggest that AF and AB will be less useful for occupational assessment, due to declining concentrations. On the other hand, the observations suggest greater clearance (including amphibole clearance) than was hitherto appreciated, and fit well the recent observations of Berry et al in Wittenoom (Australia) crocidolite miners. The already observed decrease in predicted mesothelioma cases is likely to accelerate as a result. Our findings are useful for contemporary environmental risk assessment in that, unlike past exposures, these exposures are ongoing and indeed current. The result has been that we can detect these exposures in at-risk populations either through sentinel animals (as in our observations in California) or directly in autopsy samples of general populations.

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 score0.004
Threshold uncertainty score0.280

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 head0.029
GPT teacher head0.308
Teacher spread0.279 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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