Human Exposure to Antimony. II. Contents in Some Human Tissues Often Used in Biomonitoring (Hair, Nails, Teeth)
Bibliographic record
Abstract
The presence of antimony in the human body is the result of exposure from many different sources, as is the case for any chemical element. The use of hair for diagnosing antimony exposure has become very popular because of the uncomplicated sampling and preservation, but the true utility of such studies remains uncertain. This review presents a critical discussion of the existing literature on antimony concentrations in hair, nails, and teeth, with three main objectives: (a) evaluating published data from the methodological point of view, (b) establishing a range of plausible values for antimony concentrations in these tissues, and (c) assessing statistically based correlations reported in case-control studies. From a methodological standpoint, existing data suffer from the lack of adequate certificate reference materials, low concentrations close to the detection limit of most analytical techniques and data acquisition through applying multielement techniques. These limitations are probably the underlying reason for the high dispersion of the published results and do not make it possible to establish a reliable background value for human hair from healthy, unexposed individuals. However, it is possible to estimate a concentration ceiling at 0.1 μg−1, with a probable value around 0.05 μg−1. Concerning the usefulness of antimony determinations in hair, existing results amply justify its use in occupational studies. On the other hand, the analysis of antimony concentrations in hair as an indicator of human health status does not seem to be based on any scientific evidence. The limited number of studies on human nails and teeth does not allow any conclusions to be drawn.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".