Quality Specifications for the Determination of Copper, Zinc, and Selenium in Human Serum or Plasma: Evaluation of an Approach Based on Biological and Analytical Variation
Bibliographic record
Abstract
BACKGROUND: Trace element external quality assessment schemes monitor laboratory performance and provide a stimulus for improvement in accuracy. However, monitoring of participant performance varies according to the scheme and can lead to conflicting conclusions. METHODS: Quality specifications based on biological intra- and interindividual variability were calculated and compared to those currently used by various trace element external quality assessment schemes for plasma or serum copper, zinc, and selenium concentrations. For this purpose, we evaluated results reported by participating laboratories in different schemes, at key concentrations, using z scores. RESULTS: Minimal quality specifications developed from the biological intra- and interindividual variability were, for Cu, +/-0.84 micromol/L or 12% of the assigned target concentration, whichever is greater; for Zn, +/-1.20 micromol/L or 15% of the assigned target concentration, whichever is greater; and for Se, +/-0.072 micromol/L or 12% of the assigned target concentration, whichever is greater. Reported performance of the participating laboratories depended on analyte, concentration, and the selected quality specification. In addition, the most commonly used methods for the determination of Cu, Zn, and Se may give different results. CONCLUSIONS: The proposed minimal quality specifications based on biological variation are generally slightly less stringent than those currently in use, although they do not drastically change the performance evaluation in the different schemes. These specifications are a first step in the harmonization of practices among the schemes and remain to be evaluated.
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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 | 0.065 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.002 |
| 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".