Measurement of circulating 25-hydroxyvitamin D: A historical review
Bibliographic record
Abstract
The constantly increasing requests for the measurement of serum 25-hydroxyvitamin D over the last years has led reagent manufacturers to market different automated and semi-automated methods, that being unfortunately not fully harmonized, yield different results. Liquid chromatography coupled to tandem mass spectrometry (LC/MS2) has more recently been introduced. This approach allows the distinction between the two forms of 25-hydroxyvitamin D and to measure other metabolites. This approach also requires harmonization to curtail the differences between the different analytical methods. To meet this requirement, the American National Institutes of Health (NIH), the Centre for Disease Control and Prevention (CDC) in Atlanta, the National Institute of Standards and Technology (NIST) and the vitamin D Reference laboratory of Ghent University have pooled their expertise to develop a standardization program. This article reviews the main elements and the difficulties of the automated and semi-automated methods for 25-hydroxyvitamin D, from sample preparation to the analytical phase, as well as those related to mass spectrometry. It also emphasizes the need for standardization to better define the clinical decision thresholds of vitamin D nutritional status.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".