Biomarkers of environmental manganese exposure
Bibliographic record
Abstract
We conducted a critical review on biomarkers of environmental manganese (Mn) exposure to answer the following questions: 1) are there reliable biomarkers of internal Mn exposure (Mn in biological matrices) associated with external metrics of Mn exposure (Mn in environmental media)? and 2) are there accurate reference values (RVs) for Mn in biological matrices? Three bibliographic databases were searched for relevant references and identified references were screened by two independent reviewers. Of the 6342 unique references identified, 86 articles were retained for data abstraction. Our analysis of currently available evidence suggests that Mn levels in blood and urine are not useful biomarkers of Mn exposure in non-occupational settings. The strength of the association between Mn in environmental media and saliva was variable. Findings regarding the utility of hair Mn as a biomarker of environmental Mn exposure are inconsistent. Measurements of Mn in teeth are technically challenging and findings on Mn in tooth components are scarce. In non-occupationally exposed individuals, bone Mn measurements using in vivo neutron activation analysis (IVNAA) are associated with large uncertainties. Findings suggest that Mn in nails may reflect Mn in environmental media and discriminate between groups of individuals exposed to different environmental Mn levels, although more research is needed. Currently, there is no strong evidence for any biological matrix as a valid biomarker of Mn exposure in non-occupational settings. Because of methodological limitations in studies aimed at derivation of RVs for Mn in biological materials, accurate RVs are scarce.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".