IgA Nephropathy: Epidemiology and Disease Risk Across the World
Bibliographic record
Abstract
Despite decades of research, our knowledge of the global epidemiology of IgA nephropathy remains limited. Much of what we know about IgA nephropathy incidence comes from biopsy registry studies that are subject to bias related to differences in screening programs, referral patterns, and access to healthcare. Fewer epidemiologic studies used an appropriate data infrastructure that includes a well-defined source population. Nonetheless, all these studies show considerable geographic variation in disease incidence with an increase from west to east and south to north across Eurasia. This pattern is partly explained by the distribution of genetic risk alleles in individuals of European and East Asian ancestry. Although historically thought to be an indolent disease, recent long-term follow-up studies have demonstrated an exceptionally high lifetime risk of kidney failure. The International IgA Nephropathy Prediction Tool, derived and validated in multiple ethnically diverse cohorts, has improved our ability to identify patients at high risk of progression who may benefit from therapies being tested in clinical trials. The earlier identification of high-risk patients, evaluation of novel risk factors, and accurate assessment of global disease burden require high-quality regional data infrastructures and broad collaborative efforts to ensure the impact of new treatments is maximized.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".