Comparison of a New Multiplex Immunoassay for Measurement of Ferritin, Soluble Transferrin Receptor, Retinol-Binding Protein, C-Reactive Protein and α1-Acid-glycoprotein Concentrations against a Widely-Used s-ELISA Method
Bibliographic record
Abstract
Recently, a multiplex ELISA (Quansys Biosciences) was developed that measures ferritin, soluble transferrin receptor (sTfR), retinol-binding protein (RBP), C-reactive protein (CRP), α1-acid glycoprotein (AGP), thyroglobulin, and histidine-rich protein 2. Our primary aim was to conduct a method-comparison study to compare five biomarkers (ferritin, sTfR, RBP, CRP, and AGP) measured with the Quansys assay and a widely-used s-ELISA (VitMin Lab, Willstaett, Germany) with use of serum samples from 180 women and children from Burkina Faso, Cambodia, and Malaysia. Bias and concordance were used to describe the agreement in values measured by the two methods. We observed poor overall agreement between the methods, both with regard to biomarker concentrations and deficiency prevalence estimates. Several measurements were outside of the limit of detection with use of the Quansys ELISA (total n = 42 for ferritin, n = 2 for sTfR, n = 0 for AGP, n = 5 for CRP, n = 22 for RBP), limiting our ability to interpret assay findings. Although the Quansys ELISA has great potential to simplify laboratory analysis of key nutritional and inflammation biomarkers, there are some weaknesses in the procedures. Overall, we found poor comparability of results between methods. Besides addressing procedural issues, additional validation of the Quansys against a gold standard method is warranted for future research.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".