Diagnosis and Treatment Modalities of Symptomatic Polycystic Kidney Disease
Bibliographic record
Abstract
Polycystic kidney disease (PKD) can cause end stage kidney disease with an autosomal dominant inheritance pattern. Besides renal replacement therapy or renal transplantation, there are no other curative therapies. Renal insufficiency, severe pain due to hemorrhagic expansion of the cysts, or infections are the most common clinical presentations. Diagnosis of infected cysts can be quite challenging. In recent years, 18FDG-PET/CT has shown to be the most sensitive and accurate modality for the diagnosis of infected cysts. The majority of these infections respond to systemic antibiotic therapy, but in some cases, percutaneous drainage is indicated. In some cases, the volume of the native polycystic kidneys is so extensive that native nephrectomy is necessary to create enough space in the iliac fossa to allow the placement of a renal graft. Tolvaptan, a selective arginine vasopressin V2 receptor antagonist, can be used to reduce the speed of disease progression in selected patients. Trans-arterial embolization has shown to be safe and effective to downsize very large native kidneys and it can be beneficial for patients who are at high risk for surgery or who decline surgery. The aim of our chapter is to present the current literature on the best diagnostic tests for patients with suspected infected or hemorrhagic cysts, and the best treatment modalities for patients with symptomatic polycystic kidneys prior or after renal transplantation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".