Bibliographic record
Abstract
In the past 20 years, we have witnessed tremendous advances in our ability to diagnose and treat genetic diseases of the kidney caused by complement dysregulation. Staggering progress was realized toward a better understanding of the genetic underpinnings and pathophysiology of many forms of atypical hemolytic uremic syndrome (aHUS) and C3-dominant glomerulopathies that are driven by complement system abnormalities. Many of these seminal discoveries paved the way for the design and characterization of several innovative therapies, some of which have already radically improved patients' outcomes. This review offers a broad overview of the exciting developments that have occurred in the recent past, with a particular focus on single-gene (or Mendelian), complement-driven aHUS and C3-dominant glomerulopathies that should be of interest to both nephrologists and kidney researchers. The discussion is restricted to genes with robust associations with both aHUS and C3-dominant glomerulopathies (complement factor H, complement component 3, complement factor H-related proteins) or only aHUS (complement factor B, complement factor I, and membrane cofactor protein). Key questions and challenges are highlighted, along with potential avenues for future directions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".