Clinicopathologic Significance of Predominant Lambda Light Chain Deposition in IgA Nephropathy
Bibliographic record
Abstract
Introduction IgA nephropathy (IgAN) differs from other glomerular diseases by the frequently predominant lambda over kappa light chain deposition. Using the Cure Glomerulonephropathy (CureGN) IgAN cohort, we aimed to determine whether predominant lambda chain deposition is associated with worse clinical outcomes or histopathologic markers of more active disease. Methods Patients were categorized based on the intensity of light chain staining. The lambda dominant (LD) group was defined by a difference in intensity score of lambda minus kappa ≥ 1+ and the kappa-lambda codominant (KL) group by a difference < 1+. We compared the clinical course of patients in each category from the time of kidney biopsy and time of enrollment into CureGN to the time of remission (proteinuria < 0.3 g/g), 50% reduction in estimated glomerular filtration rate (eGFR), or progression to end-stage kidney disease (ESKD). We also analyzed differences in histopathologic characteristics between the 2 groups. Results Among 440 patients, we found no significant differences between groups in baseline clinical characteristics nor in rates of remission, 50% reduction in eGFR, or progression to ESKD. Patients in the LD group had a modestly greater frequency of IgG staining ≥ 1+. The biopsy results of 234 patients reviewed by CureGN pathologists revealed a greater frequency of endocapillary hypercellularity (51.1% vs. 36.3%, P = 0.04) in the LD group, but no other significant difference in histopathologic features. Conclusion In IgAN, we found an association between lambda predominance and increased endocapillary hypercellularity, but no association with clinical outcomes.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".