Diagnostic performance of glomerular PLA2R and THSD7A antibodies in biopsy confirmed primary membranous nephropathy in South Africans
Bibliographic record
Abstract
BACKGROUND: Serum and tissue-based tests using phospholipase A2 receptor 1 (PLA2R) and thrombospondin type-1 domain containing 7A (THSD7A) are established immune biomarkers for the diagnosis of primary membranous nephropathy (PMN). This study assessed the diagnostic performance of these biomarkers in the diagnosis of PMN in South Africans. METHODS: This was a cross-sectional analysis from a single centre in Cape Town, South Africa. Relevant biodata was collected from all patients. Histology, including slides for PLA2R and THSD7A were processed and assessed by typical microscopic and immunohistochemical features. Biopsy tissues of patients with membranous lupus nephritis (LN-V) and diabetic nephropathy (DN) were used as controls. The diagnostic accuracy for diagnosis of PMN using positive PLA2R and THSD7A were evaluated. RESULTS: Of the 88 patients included, 41 had PMN with a mean age of 44.5 ± 17.5 years and 61.0% were female. Histologically, PLA2R and THSD7A were only positive in the PMN group (51.2% and 4.9%, respectively) but negative in both control groups. The sensitivity of PLA2R and THSD7A for identifying PMN was 51.2% and 4.9%, respectively. The sensitivity of both tests together was 53.7% while the specificity and positive predictive values (PPV) for any of the tests (alone or in combination) was 100%. There was no difference in the sensitivity and specificity when using PLA2R alone compared to combining the two tests (p=0.32). CONCLUSION: Glomerular staining of PLA2R and THSD7A could have potential diagnostic values in South Africans. This has implications on how immunotherapies can be initiated and used in these settings.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".