Association Between Relapse and Long-Term Kidney Outcomes in IgA Nephropathy
Bibliographic record
Abstract
Rationale & Objective: Relapse in IgA nephropathy (IgAN) remains under-researched. This study investigates the incidence of IgAN relapse, its association with adverse outcomes, and its risk factors within a prospective cohort. Study Design: Prospective cohort study. Setting & Participants: The study included 1,069 patients with IgAN who achieved remission following treatment. Predictor: Relapse or not after remission of IgAN. Outcomes: , dialysis, or kidney transplantation) or a 50% decline in eGFR. Analytical Approach: We assessed relapse incidence, its correlation with kidney endpoint events, and identified clinicopathological predictors of relapse using Cox regression analysis. Least Absolute Shrinkage and Selection Operator Cox regression was performed to screen predictive variables to predict relapse. Results: /year). Patients with corticosteroid-induced remission had a higher likelihood of relapse within 3 years. Controlling 24-hour urinary protein at remission to below 0.3 g/d was associated with a significantly improved relapse-free rate. Limitations: Single-center study; no standardized definition for IgAN remission and relapse. Conclusions: Relapse is prevalent in IgAN and is associated with an increased rate of kidney endpoint events and accelerated eGFR decline. Predictors of relapse encompass various clinicopathological indicators and medications, with stricter control of proteinuria at remission being crucial for reducing relapse rates.
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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 | 0.003 |
| 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 | 0.001 |
| 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".