Technical and clinical evaluation of anti‐ribosomal P protein immunoassays
Bibliographic record
Abstract
Autoantibodies to the three ribosomal phospho (-P) proteins P0, P1, P2, referred to as Rib-P, are specifically found in 10-40% of patients with systemic lupus erythematosus. The variations in the observed frequency of these autoantibodies is related to a number of factors such as the test system used to detect the antibodies. Several immunoassays that were designed for research and diagnostic laboratory use have been developed. The autoantigens employed in these tests include native proteins, recombinant polypeptides, and synthetic peptides. In this study, we compared the technical and clinical accuracy of anti-Rib-P antibody assays from different commercial suppliers including ELISA systems and a novel addressable laser bead assay (from Euroimmun, MBL, Pharmacia Diagnostics, INOVA). Although the assays from all suppliers used in this study performed well in the technical part of the study, relatively poor correlations and significant differences in the clinical accuracy were found. Based on the results, we conclude that the detection of anti-Rib-P antibodies strongly depends on both the nature of the antigen and the detection system. We recommend that anti-Rib-P assays should be standardized on an international level. The Varelisa Rib-P profile and the addressable laser bead Rib-P assays represent promising tools and platforms for the detection of anti-Rib-P antibodies in the future.
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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 | 0.012 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".