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Abstract A21: Environmental exposure to asbestos and mesothelioma in Colombia: A scoping review

2020· review· en· W3096373722 on OpenAlexaff
Otto Sánchez, Marcela Varona, Ana Isabel Gómez Córdoba, Leonardo Briceño-Ayala, Ángela Fernanda Espinosa

Bibliographic record

VenueCancer Prevention Research · 2020
Typereview
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsAsbestosMesotheliomaChrysotilePopulationEnvironmental healthMedicineContext (archaeology)AsbestosisGeographyPathology

Abstract

fetched live from OpenAlex

Abstract Although over 60 countries have banned asbestos worldwide, in Colombia, asbestos-containing products are still imported, chrysotile mining occurs, and industrial plants using asbestos are still active. Indeed, Colombia ranks among the top ten producers of asbestos worldwide (La Dou et al., 2010) and among the top four consumer countries in Latin America (Marsili et al., 2014, 2016). Occupational exposure to chrysotile in brake repair shops in Bogota has been documented (Cely-Garcia et al., 2012, 2016), with reports of pleural calcifications and plaques in some workers (Cely-Garcia et al., 2015), but without alterations in pulmonary function tests. Environmental exposures in communities living close to asbestos plants and mines are yet to be addressed. This scoping review of the scientific and grey literature assesses the importance of asbestos and mesothelioma in the Colombian population in the context of a permissive legislation. Our hypothesis is that, although occupational asbestos exposures occur, environmental exposures may be increasing the incidence of asbestosis and mesothelioma in the general population. According to IARC estimates, in Colombia, 88 new cases of mesothelioma were diagnosed in 2018, representing 0.1% of the total number of new cancer cases, with 72 deaths caused by this malignancy, representing 0.17% of mortality due to cancer (GLOBOCAN, 2018). Mesothelioma is more common in men with a 1.8 men:women ratio (Atlas de Mortalidad por Cancer en Colombia, 2014). However, no studies have systematically addressed the incidence of this cancer in occupational settings and in the general population. Under-reporting of the diagnosis of mesothelioma in Colombian cancer registries is possible. This is particularly important, as there are increasing clinical and community reports of nonoccupational mesothelioma cases in specific Colombian communities (Galeano, 2019). This scoping review is timely as asbestos legislation in Colombia is still evolving. To protect workers, in 1998 the Colombian Congress ratified the 1986 International Labor Organization’s agreement to prevent and control health risks due to occupational exposures to asbestos (Law 436 of 1998). The Colombian Ministries of Labor and Health followed up with legislative actions between 2001 and 2011. In contrast, to protect citizens, in 2007 the Colombian Congress considered a proposed law to ban the import, manufacturing, distribution, sale, and use of asbestos, legislation yet to be approved. However, in March 2019, in response to a lawsuit, a judge ordered the Colombian Ministries of Health and Work to create legislation to substitute asbestos nationwide by 2024. We conclude that, in Colombia, there is a lack of studies assessing asbestos exposures in workers and the general population, and that, in light of a continuous commercial use of asbestos, research on health effects, including mesothelioma incidence and prevention, is needed. Citation Format: Otto H. Sanchez, Marcela E. Varona, Ana I. Gomez, Leonardo Briceño, Angela F. Espinosa. Environmental exposure to asbestos and mesothelioma in Colombia: A scoping review [abstract]. In: Proceedings of the AACR Special Conference on Environmental Carcinogenesis: Potential Pathway to Cancer Prevention; 2019 Jun 22-24; Charlotte, NC. Philadelphia (PA): AACR; Can Prev Res 2020;13(7 Suppl): Abstract nr A21.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.842
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.156
GPT teacher head0.504
Teacher spread0.348 · 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.

Study designOther design
Domainnot available
GenreReview

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
Published2020
Admission routes1
Has abstractyes

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