Questions and Caveats in Antigen-Defined Membranous Nephropathy
Bibliographic record
Abstract
Remarkable progress has been made in the discovery of autoantigens in membranous nephropathy. With increasing testing for membranous antigens in daily practice, it is important to consider the varying strength of associations between certain antigens and underlying conditions. This review explores questions and caveats that arise when assessing results of membranous antigen testing. Specifically, we will discuss: ( 1 ) discrepancy between tissue antigen and clinical scenario, focusing on phospholipase A2 receptor; ( 2 ) one antigen≠one clinical condition, i.e ., the heterogeneity of membranous antigens seen in one clinical condition (such as in sarcoidosis), and conversely, heterogeneity of conditions associated with one antigen (such as for neural epidermal growth factor-like 1); ( 3 ) rare presence of multiple membranous-associated antigens in tissue or blood (such as with antiprotocadherin 7); and ( 4 ) lupus membranous nephritis-related antigens and their influence on diagnosis or treatment.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".