Genitourinary Interventions in Autosomal Dominant Polycystic Kidney Disease: Clinical Recommendations for Urologic and Transplant Surgeons
Bibliographic record
Abstract
Autosomal dominant polycystic kidney disease is the fourth most common single cause of end-stage renal disease worldwide with both renal and extrarenal manifestations, resulting in significant morbidity. Approaches to the management of this disease vary widely, with no broadly accepted practice guidelines. Herein, we reviewed the various surgical and interventional management options that are targeted toward treating the symptoms or addressing the resulting kidney failure. Novel treatment modalities such as celiac plexus blockade and renal denervation appear to be promising in pain relief; however, further studies are lacking. Renal cyst decortication seems to have a higher success rate in targeting cyst-related pain compared with aspiration only. In terms of requiring major surgical intervention, such as need and timing of native nephrectomy, there are several considerations when deciding on transplantation with or without a pretransplant native nephrectomy. Patients who are not candidates for native nephrectomy may consider transcatheter arterial embolization. Based on our review of the contemporary indications for genitourinary interventions in the management of autosomal dominant polycystic kidney disease, we propose an algorithm that depicts the decision-making process on assessing the indications and timing of native nephrectomy in patients with end-stage renal disease awaiting transplant.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".