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Record W2883958942 · doi:10.1093/annweh/wxy064

Retrospective Assessment of Respirable Quartz Exposure for a Silicosis Study of the Industrial Sand Industry

2018· article· en· W2883958942 on OpenAlexfundno aff
Roy J. Rando, Pamela M. Vacek, Robert E. Glenn, Cheol Woong Kwon, John E. Parker

Bibliographic record

VenueAnnals of Work Exposures and Health · 2018
Typearticle
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsnot available
FundersUniversity of British ColumbiaIndustrial Health Foundation
KeywordsSilicosisParticle sizeOccupational hygieneQuartzEnvironmental scienceEnvironmental healthParticle-size distributionOccupational exposureParticle numberParticle (ecology)ToxicologyEnvironmental engineeringMedicineEngineeringMaterials scienceMetallurgyPhysicsOccupational safety and healthGeologyNuclear physicsPathology

Abstract

fetched live from OpenAlex

Background: In 2016, the OSHA PEL for crystalline silica was reduced, renewing interest in evaluating risk of silicosis from occupational exposures. The industrial sand industry, which deals with high-purity quartz sands, is the setting for a current epidemiologic investigation of silicosis risk and progression. In support of that investigation, respirable quartz (RQ) exposures were retrospectively estimated for 67 workers with silicosis and 167 matched control workers from 21 industrial sand plants, in which some started work as early as 1929. Methods: A job exposure matrix (JEM) was constructed by integrating a modern (post-1970) RQ exposure database containing more than 40000 measurements with archival particle count exposure data from a 1947 survey. A simulation algorithm was used to develop a conversion factor to convert the archival particle count data into modern measures of RQ by randomly generating 100000 virtual dust particles of varying diameters corresponding to the size distributions of 14 archival particle size distribution samples. The equivalent respirable mass and particle counts of the virtual particles were calculated, totalled, and ratioed to derive the conversion factor. The JEM was integrated with individual job histories to calculate average and cumulative exposure for each case and control. Multiple exposure estimates were derived for unprotected exposures as well as for exposures adjusted for estimated respiratory protective equipment use and efficiency. Results: The mean of the count to respirable mass conversion factors derived from 14 archival particle size samples was 157 µg m-3 per mppcf (SD: 42; range: 96-263) with no statistical difference across process areas (drying, screening, vibrating, binning, bulk loading, bagging), P = 0.29. The JEM demonstrated an industry-wide decrease in prevailing exposures to RQ of up to about 2 orders of magnitude from the distant (1929) to the recent (2012) past. Unadjusted cumulative exposures for cases and controls were statistically different (P < 0.001) with respective medians (range) of 3764 µg m-3 year (221-25121) and 1595 µg m-3 year (0-16446). Adjustment of exposure for use of respiratory protection showed modest reductions in estimated exposure: median adjusted cumulative exposures assuming a protection factor of 5 were 86% and 77% of the unadjusted values for cases and controls, respectively. Conclusions: The industrial sand industry offers a unique setting for examination of silicosis risk because of the high silica content of industrial sand and a long history of radiographic silicosis surveillance of industry workers. However, the great majority of silicosis cases in this industry are found among former workers and are associated with exposures occurring in the distant past, which necessitates extensive retrospective exposure assessment and increases the likelihood of exposure misclassification. Nonetheless, the estimated cumulative exposures for silicosis cases and controls in this work were significantly different, with the median cumulative exposure for cases being more than twice that of their matched controls.

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 score0.012
Threshold uncertainty score0.249

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 head0.146
GPT teacher head0.418
Teacher spread0.272 · 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

Citations13
Published2018
Admission routes1
Has abstractyes

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