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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".