General Steps to Standardize the Laboratory Measurement of Serum Total 25-Hydroxyvitamin D
Bibliographic record
Abstract
The Vitamin D Standardization Program (VDSP) has collaborated with numerous groups and agencies to assemble a set of tools, i.e., a reference measurement system, that can be used to establish the traceability of 25-hydroxyvitamin D [25(OH)D] assays to relevant reference measurement procedures and reference materials. This is done with the goal of verifying end-user laboratory performance using precise statistical criteria to determine whether a specific assay is standardized. The purpose of this paper was to outline a set of steps that routine clinical and research laboratories can use to standardize their 25(OH)D assays using these tools. These steps apply to laboratories using commercially developed immunoassay measurement systems as well as in-house assays, usually based on high HPLC or LC tandem MS measurement systems. The steps are (1) initial calibration, (2) initial assessment of accuracy and bias, (3) assessment of total percent CV and mean bias, (4) use of trueness controls, and (5) participation in accuracy-based performance testing and/or external quality assessment schemes. The goal of each laboratory assay is to have a total CV of ≤10% and mean bias of ≤5%. Rigorous and less rigorous but low-cost options for meeting these statistical criteria are provided. Research laboratories who infrequently measure 25(OH)D are advised to repeat steps 1-4 for every measurement cycle. For users of commercial immunoassays who have relatively little control over standardization, we present an option for using trueness controls to develop a master equation that can be used to standardize results to the reference methods.
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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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.022 | 0.033 |
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".