Optimizing hepcidin measurement with a proficiency test framework and standardization improvement
Bibliographic record
Abstract
Objectives: Hepcidin measurement advances insights in pathophysiology, diagnosis, and treatment of iron disorders, but requires analytically sound and standardized measurement procedures (MPs). Recent development of a two-level secondary reference material (sRM) for hepcidin assays allows worldwide standardization. However, no proficiency testing (PT) schemes to ensure external quality assurance (EQA) exist and the absence of a high calibrator in the sRM set precludes optimal standardization. Methods: We developed a pilot PT together with the Dutch EQA organization Stichting Kwaliteitsbewaking Medische Laboratoriumdiagnostiek (SKML) that included 16 international hepcidin MPs. The design included 12 human serum samples that allowed us to evaluate accuracy, linearity, precision and standardization potential. We manufactured, value-assigned, and validated a high-level calibrator in a similar manner to the existing low- and middle-level sRM. Results: The pilot PT confirmed logistical feasibility of an annual scheme. Most MPs demonstrated linearity (R2>0.99) and precision (duplicate CV>12.2%), although the need for EQA was shown by large variability in accuracy. The high-level calibrator proved effective, reducing the inter-assay CV from 42.0% (unstandardized) to 14.0%, compared to 17.6% with the two-leveled set. The calibrator passed international homogeneity criteria and was assigned a value of 9.07±0.24 nmol/L. Conclusions: We established a framework for future PT to enable laboratory accreditation, which is essential to ensure quality of hepcidin measurement and its use in patient care. Additionally, we showed optimized standardization is possible by extending the current sRM with a third high calibrator, although international implementation of the sRM is a prerequisite for its success.
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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 | 0.083 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".