Recurrent and De Novo Glomerular Immune-Complex Deposits in Renal Transplant Biopsies
Bibliographic record
Abstract
CONTEXT: Recurrent and de novo glomerulonephritis is an important cause of renal allograft failure, but estimates of its prevalence vary widely. One reason for such variability is the inconsistency with which electron microscopy and immunofluorescence are used in assessing renal allograft biopsies. OBJECTIVE: To determine the prevalence of immune-complex deposits in all renal allograft biopsies performed during a 1-year period and to correlate their presence with clinical data. DESIGN: Our center accessioned a total of 118 renal allograft biopsies during 1 year from 88 patients. All biopsies were examined by both electron microscopy and immunofluorescence in addition to conventional light microscopy. Patient and donor characteristics were obtained as well as follow-up data for a minimum of 26 months after the index biopsy. RESULTS: Eight cases of immunoglobulin (Ig) A nephropathy were found (recurrent in 7 and de novo in 1). There were 9 instances of what we designate "IgM-positive immune deposits" without specific features of a recognized glomerulonephritis. To the best of our knowledge, the latter has not hitherto been described and may be part of a heterogeneous group of glomerulopathies. Other unexpected findings included de novo fibrillary glomerulonephritis and de novo membranous glomerulonephritis, the latter occurring at 3 months after engraftment. CONCLUSIONS: A high proportion (19.5%) of unselected renal allograft biopsies show immune-complex deposits both with and without a recognized glomerulopathy. These require both electron microscopy and immunofluorescence for detection. IgM-positive deposits of uncertain etiology are relatively frequent.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".