Antiphospholipase A<sub>2</sub>Receptor Autoantibodies: A Comparison of Three Different Immunoassays for the Diagnosis of Idiopathic Membranous Nephropathy
Bibliographic record
Abstract
BACKGROUND: The recent identification of circulating autoantibodies directed towards the M-type phospholipase A2 receptor (PLA2R) has been a major advancement in the serological diagnosis of idiopathic membranous nephropathy (IMN), a common cause of nephrotic syndrome in adults. The goal of this study was to compare the performance characteristics of two commercial assays as well as the first addressable laser bead immunoassay (ALBIA) developed for the detection of anti-PLA2R antibodies. METHODS: Serum samples of 157 IMN patients and 142 controls were studied. Samples were tested by a cell based immunofluorescence assay (CBA-IFA, Euroimmun, Germany), by ELISA (Euroimmun), and by a novel ALBIA employing an in vivo expressed recombinant human PLA2R. RESULTS: Overall, the three assays showed significant qualitative and quantitative correlation. As revealed by receiver operating characteristic analysis, the ALBIA correlated better with the CBA-IFA than the ELISA (P = 0.0003). The clinical sensitivities/specificities for IMN were 60.0% (51.0-68.5%)/98.6% (95.0-99.8%) and 56.2% (47.2-64.8%)/100.0% (97.4-100.0%) for ALBIA and CBA-IFA, respectively. CONCLUSION: The ALBIA represents a promising assay for the detection of anti-PLA2R antibodies showing similar performance to the CBA-IFA and the advantage of ease of use and suitability for high throughput, rapid turnaround times, and multiplexing.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".