A Comparison of Two Immunoassays for Analysing Plasma 25-hydroxyvitamin D
Bibliographic record
Abstract
A total of 1628 human plasma samples from Cycle 1 of the Canadian Health Measures Survey were assayed for total 25-hydroxyvitamin D using the DiaSorin RIA method and the Diasorin "LIAISON 25 OH Vitamin D Total" method.Bland-Altman comparison showed an average bias of 4.8 ± 16.7 nmol/L (6.3%: P<0.001) with the LIAISON method giving higher values.The relationship was investigated using linear and Deming regression.Linear regression gave: LIAISON = RIA*(0.87± 0.02) + (13.3 ± 1.2) (mean ± SE) and weighted Deming regression (constant CV) gave: LIAISON = RIA*(1.14± 0.02) -(4.2 ± 1.2).The significant deviations from a slope of unity and the significant non-zero intercepts were further investigated using non-linear regression.Quadratic regression gave: LIAISON = RIA 2 * (-0.0025 ± 0.0005) + RIA*(1.211± 0.07) + (2.9 ± 2.5).The intercept was not significantly different from 0. The quadratic equation significantly decreased the residual sum of squares (P<0.0001)indicating this model better described the relationship.Non-linearity was apparent at RIA 25-hydroxyvitamin D 110 nmol/L, where the relationship was described by: LIAISON = 97.5339+ 0.1388*RIA (r 2 = 0.0039; N.S.).However, removing points RIA 110 nmol/L did not substantially alter the regression parameters.Comparing the analytical imprecision with the total random regression error (S y/x ) suggested that sample-related effects were not present.It is recommended that cross-over analysis between these two methods include points from all parts of the range of interest to elucidate the complete nature of the relationship.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".