Biomarkers for occupational manganese exposure
Bibliographic record
Abstract
Long-term inhalation exposure to manganese (Mn) metal or its inorganic compounds can result in manganism or subclinical neurofunctional deficits. Studies have described affected workers in Mn dioxide mining, Mn-containing ore crushing and milling facilities, manufacturing of dry-cell batteries, Mn steel and alloy production plants, and in welders. The objective of this study was to critically review existing evidence on the reliability of potential biomarkers of Mn exposure, specifically the relationship between inhalation exposure to Mn particulates in different occupational settings and Mn concentrations in blood and other biological fluids and tissues, with a particular focus on whole blood as a potentially useful medium for measuring internal tissue dose. We also examined available evidence on the relationship between Mn levels in blood and adverse clinical and subclinical neurotoxic outcomes. Three bibliographic databases were searched for relevant studies and identified references were screened by two independent reviewers. Of the 6338 unique references identified, 76 articles were retained for data abstraction. Findings indicate that the relationships between Mn in blood and both external Mn exposure indices and neurofunctional impairments are limited and inconsistent. Different sources of exposure to Mn compounds, heterogeneity in the methodological approaches, and inadequate reporting of essential information limited direct comparison of the reported findings. Among the Mn-exposure biomarkers considered in this review - including biomarkers in blood, plasma, serum, erythrocytes, urine, bone, toenails, fingernails, hair, saliva - biomarkers in whole blood may provide to be most useful in Mn biomonitoring and risk assessment.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".