Immunoglobulin A Nephropathy: Advances in Understanding of Pathogenesis and Treatment
Bibliographic record
Abstract
BACKGROUND: Immunoglobulin A (IgA) nephropathy is the most common form of primary glomerulonephritis and has clinical associations with a wide range of inflammatory and infectious diseases. There is a substantial variation in clinical course and outcomes, with many patients not diagnosed until they present with sequelae, which may include gross hematuria, hypertension, renal insufficiency, and/or significant proteinuria. Treatment options are currently limited and directed mainly toward control of these sequelae and have limited ability to reduce the incidence of end-stage renal disease or treat the primary IgA defect. SUMMARY: Growing knowledge about the pathogenesis of IgA nephropathy and research into its genetic basis are helping to elucidate the course of this widely variable disease. IgA accumulation in the kidneys is thought to be the result of a number of different pathways in a "multi-hit" process that includes an initial traumatic trigger (often infection related) and subsequent memory responses that are amplified in those with a genetic predisposition to the disease and lead to an inflammatory response in susceptible individuals. Genome-wide association studies are providing new insights into the genetic variance of this autoimmune disease and are yielding information that may address both its causes and consequences. Key Messages: New treatment approaches are urgently required for the management of patients with IgA nephropathy. Novel interventions based around its inflammatory nature and "multi-hit" pathogenesis are being investigated to potentially limit disease progression.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".