Factors influencing metal concentrations in hair and nails during longitudinal follow-up of apprentice welders
Bibliographic record
Abstract
The aim of this study was to determine factors influencing observed increased metal biomarkers of exposure levels in a group of 116 Quebec apprentice welders during a longitudinal follow-up of exposure. Analysis of 14 metals was carried out in hair, fingernail, and toenail samples taken from participants over the course of their welding curriculum at 6 different times. Personal and socio-demographic characteristics, lifestyle habits, and other potential confounding factors were documented by questionnaire. Multivariate linear mixed-effect models were used to assess main predictors of metal concentrations in each biological matrix including increasing time of exposure throughout the curriculum (defined as the repeated measure “time” variable”). Significant associations between repeated measure “time” variable and metal levels in hair, fingernails, and toenails were found for chromium, iron, manganese and nickel. Significant associations with “time” were also noted for arsenic levels in hair and fingernails, and for barium, cobalt and vanadium levels in fingernails and toenails. The repeated measure “time” variable, hence increasing time of exposure throughout the curriculum, was the predominant predictor of elevated biological metal levels. Reduced spaces and simultaneous activities such as oxyfuel-cutting and welding in the same welding room were suspected to contribute to higher metal levels. Age, ethnicity, and annual household income exerted an effect on metal levels and considered as confounders in the models. Variations observed in metal levels between hair and nails of apprentice welders also emphasized the relevance and importance of performing multi-matrix and multi-element biomonitoring to assess temporal variations in biological metal concentrations during welding curriculum.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".