Urinary Metal Concentrations Among Female Welders
Bibliographic record
Abstract
As part of a Canada-wide study of women entering non-traditional trades [Women's Health in Apprenticeship Trades-Metalworkers and Electricians (WHAT-ME)], we examined spot urine samples from women welders in Alberta to determine whether urinary metal concentrations exceeded those of the general population, to compare levels to previously published urinary concentrations in male welders and to examine the relationship with welding tasks. Women mailed-in urine samples collected close to the time of completing a detailed exposure questionnaire, including welding tasks on their most recent day welding at work. Of 53 welders working in their trade, 45 had urinary creatinine >0.3-≤3.0g l(-1) and were included in analyses. Seven metals were examined for which both population and male welder urinary concentrations were available: cadmium, chromium, cobalt, copper, manganese, nickel, and zinc. Principal component analysis was used to extract three components from natural log transformed creatinine-corrected metal concentrations. Of the 45 women, 17 reported more than one main task. Overall two thirds worked in fabrication, a third on pipe welding, and smaller numbers on repair, in construction or other tasks: manual metal arc welding was reported by 62%, semi-automatic arc welding by 47%, and arc welding with a tungsten electrode by 15%. In multiple regression analyses, little relation was found between urinary metals and task or type of welding, except for cadmium where lower levels were seen in those reporting semi-automatic manual welding (after adjustment for age and smoking). The proportion of women welders exceeding the selected general population 95th percentile was high for manganese (96%) and chromium (29%). Urinary metal concentrations were similar to those reported for male welders with only manganese, with a geometric mean in women of 1.91 µg g(-1) creatinine, and perhaps copper (11.8 µg g(-1) creatinine), consistently lower in male welders. Although not evident from the task analysis reported here, differences in exposure by sex may be explained by type of welding or by other work practices. A closely comparable cohort of male welders would be necessary to examine this hypothesis more fully.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 teacher head, 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".