A comparison of clinical laboratory data for assigning a consensus value for manganese in a caprine blood reference material
Bibliographic record
Abstract
Biomonitoring for manganese (Mn) exposure is important due to its potential to cause adverse health effects. In this study, we investigate how different sample preparation methods (dilution, digestion), calibration protocols (aqueous, blood-based, standard additions), and instrumental techniques affect Mn method bias and analytical imprecision. The techniques used included graphite furnace atomic absorption spectrometry (GFAAS), dynamic reaction cell inductively coupled plasma mass spectrometry (DRC-ICP-MS), and sector field (SF-) ICP-MS. We analyzed NIST SRM 1643e Trace Elements in Water and SRM 1598a Inorganic Constituents in Animal Serum (both certified for Mn), and SRM 955c Toxic Metals in Caprine Blood – Level 1 (not certified for Mn). Various matrix effects in ICP-MS produced inaccurate results for SRM 1643e and discrepant results for SRM 955c. In the absence of a certified value for Mn in SRM 955c, we assigned a “consensus” value by combining data from the New York State Department of Health (NYS), the Centers for Disease Control and Prevention (CDC) and the Centre de toxicologie du Québec (CTQ). With this interlaboratory approach, we established an “all-lab” consensus value of 16.3 ± 0.8 μg L−1 based on data from DRC-ICP-MS with dilution sample preparation and blood-based calibration. We also assigned an “all-method” consensus value of 16.3 ± 0.9 μg L−1 based on GFAAS and SF-ICP-MS data from the NYS lab and the DRC-ICP-MS all-lab consensus value. Although the expanded uncertainty (U) calculated for the consensus values may not fully account for all sources of uncertainty, it does show the relative variation that might be expected from one study to the next for the determination of Mn in blood.
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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 | 0.028 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".