Exposure of the general French population to metals and metalloids in 2014–2016: Results from the Esteban study
Bibliographic record
Abstract
The purpose of the Esteban study was to describe levels of various biomarkers of exposure to several environmental pollutants, including metals and metalloids, among the French population. This paper describes the distribution of concentrations of 28 metals and metalloids in two different populations, and estimates the main determinants of exposure to total arsenic, the sum of inorganic arsenic (iAs) and its two metabolites monomethylarsonic acid (MMA) and dimethylarsinic acid (DMA), cadmium, chromium, copper, mercury and nickel. Esteban is a cross-sectional study conducted between 2014 and 2016 on a random sample of 2503 adults (18–74 years old) and 1104 children (6–17 years old) from the general population. The data collected included biological samples (blood, hair, and urines), socio-demographic characteristics, environmental and occupational exposure, and information on dietary factors and lifestyle. The geometric mean and percentiles of the distribution were estimated for each metal. Multivariate analyses were performed to identify the determinants of exposure using a generalized linear model. Only four metals had a quantification rate below 90% in adults (beryllium, iridium, palladium, and platinum), and three metals in children (beryllium, iridium, and platinum). The concentrations of total arsenic, cadmium, chromium and mercury were higher than those found in most international studies. The determinants significantly associated with exposure were mainly diet and smoking. Esteban provided a nationwide description of 28 metal and metalloid exposure levels for adults (some never measured before) and for the first time in children. The study results highlighted widespread exposure to several metals and metalloids. These results could be used to advocate public health decisions for continued efforts to reduce harmful exposure to toxic metals. The Reference values (RV95) built from Esteban could also be used to support future government strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".