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Record W2118058055 · doi:10.1097/jom.0000000000000134

Do Existing Empirical Models for Welding Fumes Estimate Exposure to Ultrafine Particles Among Canadian Welding Apprentices?

2014· letter· en· W2118058055 on OpenAlexafffundabout
Eva Suarthana, Maximilien Debia, Igor Burstyn, Hans Kromhout

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2014
Typeletter
Languageen
FieldMedicine
TopicOccupational exposure and asthma
Canadian institutionsUniversité de MontréalHôpital du Sacré-Cœur de MontréalFonds de Recherche du Québec - Santé
FundersUniversité de Montréal
KeywordsWeldingMedicineApprenticeshipMetallurgyMaterials science

Abstract

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To the Editor: Welders are at risk of a wide range of respiratory health problems, including bronchitis, airway irritation, and lung function changes.1–5 Despite short duration of exposure, an inception cohort study of apprentice welders in Quebec documented a significant respiratory function decline, incidence of welding-related respiratory symptoms suggestive of occupational asthma and sensitization to metallic salts.6 It is important to understand how these effects relate to exposures experienced by apprentices in order to develop adequately protective exposure standards. Welders may be exposed to numerous chemical hazards associated with welding and cutting processes, including welding fumes, inert gases, gas mixtures, and solvents.7 Welding fumes consist of metallic oxides and gaseous vapors as well as ultrafine particles (UFP; size <100 nm).8 The type and quantity of fumes generated greatly depend on many factors, including (but not limited to) the welding process, and within the process, electrodes, fluxing agents, coatings on the base metal, base metals, and the power configuration of the welding machine.9 An exposure study in Quebec showed that apprentices in welding profession have a high level of exposure to UFP during the whole training period.10 Nevertheless, investigation of welding exposure at vocational schools is hardly performed. Empirical exposure models may be useful tools to provide estimates of exposure levels in this setting. Several models for estimating welding exposure exist11–13 and have been used to investigate associations between welding exposure and respiratory symptoms.5 These models were developed and validated using mass concentration data without consideration of the UFP fraction of the aerosol. Moreover, these exposure models have never been applied in the population of apprentices. Thus, the objective of this study was to evaluate how well existing empirical models for welding fumes estimate exposure to UFP among welding apprentices. We used an existing exposure database of 136 UFP measurements collected by Debia and colleagues10 from two welding vocational schools in Quebec. We used three exposure models (Table 1)11–13 to estimate UFP exposure among the apprentice welders studied by Debia and colleagues.10 The first model was developed by Kromhout and colleagues11 for inhalable dusts and fumes; the second model by Lehnert and colleagues12 for respirable dusts and fumes; and the third model by Liu and colleagues13 for the total particulate matter. Pearson correlation coefficients (rp) were calculated between the estimated exposure to welding fumes on the basis of the three models and measured UFP concentrations.14 All analyses were performed using SPSS 20.0 for Windows (Statistical Package for Social Sciences, Chicago, IL).TABLE 1: Exposure Models for Welding ExposureAs shown in Table 2, we found low correlation coefficients between the measured UFP concentrations and the estimated welding fume concentrations from the three exposure models that ranged from 0.11 to 0.22. Lehnert and colleagues12 found a higher correlation coefficient (0.42) between ultrafine and respirable particles in welding fumes. Low correlations may be found because different components of welding fumes have different predictors. It is also important to note that UFP concentrations were derived in a standard apprentices' cohort, whereas the exposure models were derived in a large group of welders; the two populations have very different exposure profiles in terms of duration of exposure and welding frequency.TABLE 2: Correlation Coefficients (r p) of the Estimated Exposure to Welding Fumes and the Measured UFP ConcentrationCorrelations coefficients between the estimates of the three models were much higher and ranged from 0.41 to 0.74 (Table 2). This finding was somewhat expected. According to Lenhert et al,12 respirable particles comprised about half of the mass of the inhalable particles in the welding fume. Measuring it with a respirable, inhalable, or total dust sampler will therefore not result in differences in estimated concentrations. In conclusion, current empirical models for exposure to welding fumes are insufficient for predicting exposure to UFP among welding apprentices. More UFP measurements are needed to derive UFP-specific empirical models. These models are crucial for controlling exposure, which is of increasing importance as evidence suggests that UFP may contribute to adverse respiratory and cardiovascular outcomes.15 Eva Suarthana, MD, PhD Research Centre, Hôpital du Sacré-Coeur de Montréal Montreal, Quebec, Canada Department of Social and Preventive Medicine, Université de Montréal, Canada Maximilien Debia, PhD Department of Environmental and Occupational Health, Université de Montréal, Canada Igor Burstyn, PhD Drexel University School of Public Health, Philadelphia, Penn. Hans Kromhout, PhD Institute for Risk Assessment Sciences, Utrecht University, the Netherlands ACKNOWLEDGMENTS We thank Denyse Gautrin of the Université de Montréal for her critical review of the manuscript.

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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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.180
Threshold uncertainty score1.000

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)0.0000.000

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 head0.078
GPT teacher head0.340
Teacher spread0.262 · 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 designNot applicable
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".

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Citations0
Published2014
Admission routes3
Has abstractyes

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