The relationship between overactive bladder and prostate cancer: A scoping review
Bibliographic record
Abstract
INTRODUCTION: The relationship between prostate cancer (PCa) and overactive bladder (OAB) is poorly understood. PCa and OAB are frequently diagnosed in elderly populations, so it could be expected that both conditions would be observed in older patients. Whether PCa and OAB occur independently with age, or the presence of PCa leads to the onset of OAB/lower urinary tract symptoms (LUTS) has not been explored. This review aimed to investigate whether men newly diagnosed with PCa are more likely to have OAB compared to the general population, and if the various treatment modalities for PCa are likely to impact the incidence or exacerbation of OAB. METHODS: The University of Calgary's databases for Medline and PubMed were searched for relevant publications. No restrictions were placed on the study design reported. Any publications reporting OAB and a PCa diagnosis and/or observation relating to PCa diagnosis and rates of OAB/LUTS in an adult population were included for full review. RESULTS: Of the studies examining the relationship between PCa and LUTS, results varied, but frequently indicated an inverse association between PCa and LUTS in which patients newly diagnosed with PCa were more unlikely to have LUTS compared to the general population. Following treatment, brachytherapy resulted in a higher prevalence of OAB symptoms compared to surgical treatment and external beam radiation therapy. CONCLUSIONS: Diverse evidence was found regarding the relationship between the prevalence of pre-treatment OAB and PCa diagnosis. However, limited evidence, as well as uncertainty regarding pre-treatment symptoms and their impact on post-treatment outcomes, restricts potential conclusions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".