Evaluation of automated methods for quantifying serum 25‐hydroxyvitamin D
Bibliographic record
Abstract
Overview: The accepted biomarker of vitamin D nutritional status is serum 25‐hydroxyvitamin D [25(OH)D]. However, a clear understanding of vitamin D research is confounded by the variability among assays used for measuring 25(OH)D. Objectives: To compare two new automated assays with the well‐established DiaSorin radioimmunoassay (RIA) for 25(OH)D. Methodology: The 25(OH)D from human sera (n=160) was quantified using the RIA and two automated non‐radioactive immunoassays: DiaSorin Liaison â Total 25(OH)Dâ and Roche â Elecsys 25‐OHâ. Correlation was assessed with the Pearson test and agreement with the Bland‐Altman method. Results: The RIA and Liaison correlated well ( r = 0.919) and with negligible bias (bias +/â 95% limits of agreement = â 0.72 +/â 16.01nmol/L). The Roche correlated similarly with both the RIA and Liaison but with higher bias versus the RIA ( r = 0.871; bias = â 2.55 +/â 21.06 nmol/L) than the Liaison ( r = 0.861; bias = â1.72 +/â 21.34 nmol/L). Imprecision (CV%) within‐run for the RIA, Liaison and Roche was 9.66%, 6.29%, and 4.52%, and between‐run was 10.8%, 6.67%, and 9.13%, respectively. Conclusion: For quantification of serum 25(OH)D, the Liaison â Total 25(OH)Dâ demonstrated a stronger correlation and better agreement with the reference method (RIA) than the Roche â Elecsys 25‐OHâ.
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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 | 0.031 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".