Management of Medium and Long Term Complications Following Prostate Cancer Treatment Resulting in Urinary Diversion – A Narrative Review
Bibliographic record
Abstract
The purpose of this narrative review is to discuss and highlight recently published studies regarding the surgical management of patients suffering from prostate cancer treatment complications. Focus will be put on the recalcitrant and more complex cases which might lead to urinary diversion as a definite, last resort treatment. It is in the nature of every treatment, that complications will occur and be bothersome for both patients and physicians. A small percentage of patients following prostate cancer treatment (radical prostatectomy, radiation therapy, or other focal therapies) will suffer side effects and thus, will experience a loss of quality of life. These side effects can persist for months and even years. Often, conservative management strategies fail resulting in recalcitrant recurrences. Prostate cancer patients with "end-stage bladder," "devastated outlet," or a history of multiple failed interventions, are fortunately rare, but can be highly challenging for both patients and Urologists. In a state of multiple previous surgical procedures and an immense psychological strain for the patient, urinary diversion can offer a definite, last resort surgical solution for this small group of patients. Ideally, they should be transferred to centers with experience in this field and a careful patient selection is needed. As these cases are highly complex, a multidisciplinary approach is often necessary in order to guarantee an improvement of quality of life.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".