Derivation of whole blood biomonitoring equivalents for titanium for the interpretation of biomonitoring data
Bibliographic record
Abstract
Biomonitoring equivalents (BEs) have been increasingly applied for biomonitoring purposes by regulatory bodies worldwide. The present report describes the development of a BE for titanium based on a 4-step process: (i) identification of a critical study/point of departure (PoD) supporting an established oral exposure guidance value (OEGV);, (ii) review the available oral PK data and application of a pharmacokinetic model for titanium; (iii) selection of the most appropriate biomarker of exposure in a specific tissue and calculation of steady-state tissue levels corresponding to the PoD in the critical study; and (iv) derivation of BE value adjusting for the uncertainties considered in the original OEGV assessment. Using the above 4-step approach, a blood BE value of 32.5 μg titanium/L was derived. Key components of the analysis included a pharmacokinetic model developed by investigators at the Netherlands National Institute of Public Health (RIVM) and a two-year rodent bioassay of titanium conducted by the US National Cancer Institute. The most sensitive pharmacokinetic parameter involved in the current BE derivation is the oral absorption factor of 0.02%. The provisional BE proposed in this article may be updated as new information on the pharmacokinetics of titanium becomes available.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".