An assessment of clinical laboratory performance for the determination of manganese in blood and urine
Bibliographic record
Abstract
BACKGROUND: Proficiency testing or external quality assessment schemes (PT/EQASs) are an important method of assessing laboratory performance. As each scheme establishes assigned values and acceptable ranges for the analyte according to its own criteria, monitoring of participant performance varies according to the scheme and can lead to conflicting conclusions. METHODS: Standard deviations (SDs) for PT were derived from Thompson's and biological variation models applied to blood and urine manganese (Mn) robust data from four EQASs from North America and Europe. The fitness for purpose was verified by applying these SDs to individual results. RESULTS: Using Thompson characteristic function the relationship between SD and Mn concentration, expressed in nmol/L was the square root of [19.72+(0.07712×Mn concentration2)] for blood and the square root of [6.772+(0.09852×Mn concentration2)] for urine. While the biological variation model was not suitable for urine, it produced an acceptable range for blood as ±54.4 nmol/L (assigned value ≤320 nmol/L) or 17% (assigned value >320 nmol/L). For blood, individual performance evaluated by the two approaches led to similar conclusions. CONCLUSIONS: The biological variation model can be used to propose quality specifications for blood, however it could not be applied to urine. The Thompson characteristic function model could be applied to derive quality specifications for Mn in urine and, to a lesser extent in blood. The more lenient quality specifications for blood highlight the difficulty of determining Mn in this matrix. Further work is needed to harmonize PT, such as using assigned ranges for the specimens.
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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 | 0.041 | 0.061 |
| 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 | 0.001 |
| 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 | 0.000 |
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 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".