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Record W3087176478 · doi:10.1016/j.envint.2020.106089

The effect of occupational exposure to welding fumes on trachea, bronchus and lung cancer: A protocol for a systematic review and meta-analysis from the WHO/ILO Joint Estimates of the Work-related Burden of Disease and Injury

2020· review· en· W3087176478 on OpenAlexaff
Frank Pega, Nicholas Chartres, Neela Guha, Alberto Modenese, Rebecca L. Morgan, Martha Sílvia Martinez-Silveira, Dana Loomis

Bibliographic record

VenueEnvironment International · 2020
Typereview
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsMcMaster UniversityImpact
FundersNational Institute for Occupational Safety and HealthUniversity of Nevada, RenoInternational Labour OrganizationWorld Health Organization
KeywordsInternational agencySystematic reviewMedicineLung cancerOccupational injuryMeta-analysisMEDLINEEnvironmental healthCancerPathologyPoison controlInjury preventionPolitical scienceInternal medicine

Abstract

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BACKGROUND: The World Health Organization (WHO) and the International Labour Organization (ILO) are developing joint estimates of the work-related burden of disease and injury (WHO/ILO Joint Estimates), with contributions from a large network of experts. Welding fumes have been classified as carcinogenic to humans (Group 1) by the International Agency for Research on Cancer (IARC); this assessment found sufficient evidence from studies in humans that welding fumes are a cause of lung cancer. In this article, we present the protocol for a systematic review of parameters for estimating the number of deaths and disability-adjusted life years from trachea, bronchus and lung cancer attributable to occupational exposure to welding fumes, to inform the development of the WHO/ILO Joint Estimates. OBJECTIVES: We aim to systematically review and meta-analyse estimates of the effect of occupational exposure to welding fumes on trachea, bronchus and lung cancer, applying the Navigation Guide systematic review methodology as an organizing framework. DATA SOURCES: We will search electronic bibliographic databases for potentially relevant records from published and unpublished studies, including Medline, EMBASE, Web of Science, and CISDOC. We will also search electronic grey literature databases, Internet search engines and organizational websites; hand search reference list of previous systematic reviews and included study records; and consult additional experts. STUDY ELIGIBILITY AND CRITERIA: We will include working-age (≥15 years) workers in the formal and informal economy in any Member State of WHO and/or ILO but exclude children (<15 years) and unpaid domestic workers. The eligible risk factor will be occupational exposure to welding fumes, measured directly or indirectly (i.e., through proxy of relevant occupation, work task, job-exposure matrix, expert judgment or self-report). The eligible outcomes will be trachea, bronchus and lung cancer. We will include randomized controlled trials, cohort studies, case-control studies and other non-randomized intervention studies with an estimate of the relative effect of any occupational exposure to welding fumes on the prevalence of, incidence of or mortality from trachea, bronchus and lung cancer, compared with the theoretical minimum risk exposure level of no occupational exposure to welding fumes. STUDY APPRAISAL AND SYNTHESIS METHODS: At least two review authors will independently screen titles and abstracts against the eligibility criteria at a first stage and full texts of potentially eligible records at a second stage, followed by extraction of data from qualifying studies. Two or more review authors will assess risk of bias and the quality of evidence, using the Navigation Guide tool or approach. If feasible, we will combine relative risks using meta-analysis. We will report results using the preferred reporting items for systematic reviews and meta-analyses guidelines (PRISMA).

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)0.077
metaresearch head score (Gemma)0.137
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.077
Threshold uncertainty score0.407

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0770.137
Meta-epidemiology (narrow)0.0070.005
Meta-epidemiology (broad)0.0200.025
Bibliometrics0.0170.015
Science and technology studies0.0040.004
Scholarly communication0.0070.007
Open science0.0060.005
Research integrity0.0090.007
Insufficient payload (model declined to judge)0.0510.007

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 head0.035
GPT teacher head0.359
Teacher spread0.323 · 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 designSystematic review
Domainnot available
GenreProtocol

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

Citations30
Published2020
Admission routes1
Has abstractyes

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