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Record W2054832079 · doi:10.1179/107735211799030951

Asbestos-Related Disease in Banlgadeshi Ship Breakers: A Pilot Study

2011· article· en· W2054832079 on OpenAlexaff
Midori N. Courtice, Paul A. Demers, Tim K. Takaro, Sverre Vedal, Sk Akhtar Ahmad, Hugh Davies, Zakia Siddique

Bibliographic record

VenueInternational Journal of Occupational and Environmental Health · 2011
Typearticle
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsSimon Fraser UniversityUniversity of British Columbia
Fundersnot available
KeywordsAsbestosAsbestosisEnvironmental healthOccupational safety and healthMedicineOccupational diseaseOccupational medicineDiseaseOccupational exposurePathology

Abstract

fetched live from OpenAlex

A pilot study tested the feasibility of conducting occupational health research in Bangladesh while examining prevalence of asbestos-related diseases including asbestosis, work-related respiratory symptoms, and attitudes to occupational health and safety among a group of internal migrant ship breakers. Data was collected on clinical and work history, respiratory symptoms, and occupational health and safety practices in Bengali. A B-reader read all postero-anterior chest x-rays. In the 104 male ship breakers studied, prevalence of asbestos-related disease was 12%, of which asbestosis accounted for 6%. Knowledge of asbestos and occupational health and safety measures were almost nonexistent. The prevalence of asbestos-related diseases is low compared to studies in shipbuilders and repairers, but a risk underestimate could have resulted from challenges identified during study design and implementation including: industry noncooperation and a culture of corruption; technological and language barriers; and a regional lack of physician knowledge and research on occupational diseases.

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)0.002
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.059
GPT teacher head0.323
Teacher spread0.264 · 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

Citations15
Published2011
Admission routes1
Has abstractyes

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