The association between amalgam dental surfaces and urinary mercury levels in a sample of Albertans, a prevalence study
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to quantify the relationship between number of dental amalgam surfaces and urinary mercury levels. METHODS: This study uses participant data from a large philanthropic chronic disease prevention program in Calgary, Alberta, Canada. Urine samples were analysed for mercury levels (measured in μg/g-creatinine). T-tests were used to determine if differences in urine mercury were statistically significant between persons with no dental amalgam surfaces and one or more dental amalgam surfaces. Linear regression was used to estimate the change in urinary mercury per amalgam surface. RESULTS: Urinary mercury levels were statistically significantly higher in participants with amalgam surfaces, with an average difference of 0.55 μg/g-creatinine. Per amalgam surface, we estimated an expected increase of 0.04 μg/g-creatinine. Measured urinary mercury levels were also statistically significantly higher in participants with dental amalgam surfaces following the oral administration of 2,3-dimercaptopropane-l-sulfonate (DMPS) and meso-2,3-dimercaptosuccinic acid (DMSA) which are used to mobilize mercury from the blood and tissues. DISCUSSION: Our estimates indicate that an individual with seven or more dental amalgam surfaces has 30% to 50% higher urinary mercury levels than an individual without amalgams. This is consistent with past literature that has identified seven amalgam surfaces as an unsafe level of exposure to mercury vapor. Our analysis suggests that continued use of silver amalgam dental fillings for restorative dentistry is a non-negligible, unnecessary source of mercury exposure considering the availability of composite resin alternatives.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".