A systematic review and meta-analysis of human biomonitoring studies on exposure to environmental pollutants in Iran
Bibliographic record
Abstract
Population exposure to environmental contaminants can be precisely observed through human biomonitoring studies. The present study aimed to systematically review all the biomonitoring studies conducted in Iran on some selected carcinogen environmental pollutants. In this systematic review study, 11 carcinogen agents were selected including arsenic, cadmium, chromium, nickel, lindane, benzene, trichloroethylene (TCE), pentachlorophenol (PCP), radon-222, radium-224, - 226, - 228, and tobacco smoke. The Web of Science, PubMed, and Scopus databases were searched for peer-reviewed articles published in English. After several screening steps, data were extracted from the studies. Meta-analyses (a random-effect model using the DerSimonian-Laired method) were performed only for the biomarkers with more than three eligible articles, including cadmium in blood and breast milk, and arsenic in breast milk. Methodological quality of the studies was assessed using the Newcastle-Ottawa Quality Assessment Scale adapted for cross-sectional studies. Of the 610 articles found in the database search, 30 studies were eligible for qualitative review, and 13 were included in the meta-analysis (cadmium in blood (n = 3), cadmium in breast milk (n = 6), and arsenic in breast milk (n = 4)). The overall pooled average concentrations (95% CI) of cadmium in blood, cadmium in breast milk, and arsenic in breast milk were 0.11 (95% CI: 0.08, 0.14), 5.38 (95% CI: 3.60, 6.96), and 1.42 (95% CI: 1.02, 1.81) µg/L, respectively. These values were compared with the biomarker concentrations in other countries and health-based guideline values. This study showed that there is a need for comprehensive action plans to reduce the exposure of general population to these environmental contaminants.
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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 | 0.019 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.027 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".