Mayo Clinic Consensus Report on Membranous Nephropathy: Proposal for a Novel Classification
Bibliographic record
Abstract
Membranous nephropathy (MN) is a pattern of injury caused by autoantibodies binding to specific target antigens, with accumulation of immune complexes along the subepithelial region of glomerular basement membranes. The past 20 years have brought revolutionary advances in the understanding of MN, particularly via the discovery of novel target antigens and their respective autoantibodies. These discoveries have challenged the traditional classification of MN into primary and secondary forms. At least 14 target antigens have been identified, accounting for 80%-90% of cases of MN. Many of the forms of MN associated with these novel MN target antigens have distinctive clinical and pathologic phenotypes. The Mayo Clinic consensus report on MN proposes a 2-step classification of MN. The first step, when possible, is identification of the target antigen, based on a multistep algorithm and using a combination of serology, staining of the kidney biopsy tissue by immunofluorescence or immunohistochemistry, and/or mass spectrometry methodology. The second step is the search for a potential underlying disease or associated condition, which is particularly relevant when knowledge of the target antigen is available to direct it. The meeting acknowledges that the resources and equipment required to perform the proposed testing may not be generally available. However, the meeting consensus was that the time has come to adopt an antigen-based classification of MN because this approach will allow for accurate and specific MN diagnosis, with significant implications for patient management and targeted 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 | 0.039 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".