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Record W2624913702 · doi:10.1038/bjc.2017.161

Prioritising action on occupational carcinogens in Europe: a socioeconomic and health impact assessment

2017· article· en· W2624913702 on OpenAlexaff
John W. Cherrie, Sally Hutchings, Melanie Gorman Ng, Ritesh Mistry, C Corden, Judith Lamb, Araceli Sánchez Jiménez, Amy L. Shafrir, M Sobey, Martie van Tongeren, Lesley Rushton

Bibliographic record

VenueBritish Journal of Cancer · 2017
Typearticle
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsAmec Foster Wheeler (Canada)
FundersCranfield UniversityEuropean CommissionUniversity of Warwick
KeywordsEnvironmental healthDirectiveHealth impact assessmentSocioeconomic statusPublic healthPsychological interventionEuropean unionBusinessLegislatureMedicinePublic economicsPolitical sciencePopulationEconomicsNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Work-related cancer is an important public health issue with a large financial impact on society. The key European legislative instrument is the Carcinogens and Mutagens Directive (2004/37/EC). In preparation for updating the Directive, the European Commission commissioned a study to provide a socioeconomic, health and environmental impact assessment. METHODS: The evaluation was undertaken for 25 preselected hazardous substances or mixtures. Estimates were made of the number of cases of cancer attributable to workplace exposure, both currently and in the future, with and without any regulatory interventions, and these data were used to estimate the financial health costs and benefits. RESULTS: It was estimated that if no action is taken there will be >700 000 attributable cancer deaths over the next 60 years for the substances assessed. However, there are only seven substances where the data suggest a clear benefit in terms of avoided cancer cases from introducing a binding limit at the levels considered. Overall, the costs of the proposed interventions were very high (up to [euro ]34 000 million) and the associated monetised health benefits were mostly less than the compliance costs. CONCLUSIONS: The strongest cases for the introduction of a limit value are for: respirable crystalline silica, hexavalent chromium, and hardwood dust.

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 score0.146
Threshold uncertainty score0.238

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 head0.056
GPT teacher head0.441
Teacher spread0.385 · 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

Citations36
Published2017
Admission routes1
Has abstractyes

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