Clinical uptake of an antigen-based approach to membranous nephropathy: a survey of general nephrologists and glomerular disease experts
Bibliographic record
Abstract
BACKGROUND: In recent years, there has been an emergence of new antigens discovered in membranous nephropathy (MN). Whether these antigens have impacted the approach to, and management of, MN patients undertaken by nephrologists is still unclear. METHODS: We conducted a cross-sectional international survey pertaining to 13 antigens recently discovered in MN. The survey was distributed by the National Kidney Foundation, direct emails, and social media. RESULT: PLA2R, THSD7A, NELL1, and EXT1/2 testing were readily available while the most common response for other antigen testing was 'Not Performed' or 'Unknown'. All respondents had tested for or treated PLA2R-positive MN. Of 79 respondents, only 12.7% had treated THSD7A, 15.2% for NELL1 and 6.3% for EXT1/2 positive MN. For PLA2R, THSD7A, and NELL1, a majority chose rituximab (75.4, 87.5, and 80.0%, respectively) as initial treatment, and would treat with immunosuppression before completing 6 months of conservative therapy. A majority of respondents would routinely or occasionally omit a kidney biopsy in the setting of positive serum anti-PLA2R antibodies, however, 27.5% would rarely do so. There was no clear consensus across respondents regarding the use of anti-PLA2R serum levels in determining remission. CONCLUSION: Although many new MN antigens have been discovered, there is limited availability of tests identifying these less common antigens. While the survey suggests potential for utilization of an antigen-tailored approach based on identified differences in screening and treatment practices, there remains a lag in the full adoption of this new information. Further progress in accessibility of antigen testing and research into antigen associations will enable a more individualized approach to the management of MN.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".