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Record W3200326428 · doi:10.1097/jom.0000000000002375

Break-even Analysis of Respirable Crystalline Silica (RCS) Exposure Interventions in the Construction Sector

2021· article· en· W3200326428 on OpenAlexaff
Emile Tompa, Amirabbas Mofidi, Chaojie Song, Victoria H Arrandale, Katherine J. Jardine, Hugh Davies, Thomas Tenkate, Paul A. Demers

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsMcMaster UniversityInstitute for Work & HealthOccupational Cancer Research CentreUniversity of British ColumbiaPublic Health OntarioToronto Metropolitan UniversityUniversity of TorontoCanadian Cancer Society
Fundersnot available
KeywordsPsychological interventionRespiratorEnvironmental healthMedicineEnvironmental scienceToxicologyMaterials science

Abstract

fetched live from OpenAlex

OBJECTIVE: We investigated long-term economic impacts of respirable crystalline silica (RCS) removal interventions in the construction at the societal level. METHODS: We estimated costs and benefits of two RCS exposure interventions, use of "respirators" and "wet method," over a 30-year time period. We identified economic impacts of the interventions under four different scenarios. RESULTS: Under current practices, we estimated that approximately 125 lung cancer cases attributable to RCS exposure would arise in 2060. Under the full exposure removal scenario, we estimated there would be 53 new cases. Over the 30-year time period, the estimated cumulative averted cases are 787 and 482 for respirators and wet method, respectively, which amount to net benefits of $422.13 and $394.92 million. CONCLUSIONS: Findings provide important information for policymakers seeking to reduce the economic burden of occupational lung cancer in society.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.004
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0130.000

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 head0.038
GPT teacher head0.306
Teacher spread0.268 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2021
Admission routes1
Has abstractyes

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