Treatment of Autosomal-Dominant Polycystic Kidney Disease
Bibliographic record
Abstract
Autosomal-dominant polycystic kidney disease (ADPKD) is a chronic systemic disease that affects all races and ethnicities. It is the fourth leading cause of end-stage kidney disease, and it has a heterogenous phenotype ranging from mild to severe disease. Identifying patients with ADPKD who are at risk of rapid progression can guide therapeutic decisions. Several tools to predict disease severity are available, based on features such as total kidney volume assessed with magnetic resonance imaging, PKD genotype, estimated glomerular filtration rate (eGFR) trajectory, and the occurrence of hypertension and urologic complications early in life. During the past decade, more evidence has emerged regarding optimal ADPKD management. The HALT PKD (Halt Progression of Polycystic Kidney Disease) trial supported intensive blood pressure control in patients younger than 50 years of age with preserved kidney function. A healthy lifestyle, including maintaining a healthy weight, salt restriction, and smoking cessation, is likely to be beneficial. Tolvaptan, the only disease-modifying agent for patients with ADPKD at risk of rapid progression, is gaining wider use, but is still limited by its side effects. This is an exciting time for the ADPKD community because multiple promising interventions are in the pipeline and being investigated.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".