Revisiting an Old Conundrum: A Systematic Review and Meta-Analysis of Intravesical Therapy for Treatment of Urothelial Carcinoma of the Prostate
Bibliographic record
Abstract
BACKGROUND: The optimal management of non-invasive (mucosal and/or ductal) urothelial carcinoma of the prostate remains elusive and there is a paucity of data to guide treatment. OBJECTIVE: Our objective was to systematically review and synthesize treatment responses to conservative management of non-invasive prostatic urothelial carcinoma using intravesical therapy. METHODS: A systematic literature search using MEDLINE, EMBASE, Cochrane Library, SCOPUS, and Web of Science databases from inception to November 2019 was performed. Risk of bias assessment was performed using the Newcastle-Ottawa scale for non-randomised studies. Pooled estimates of complete response in the bladder and prostate and prostate only were performed using a random effects model. Pre-specified subgroup analyses were generated to assess differences in complete responses for: BCG therapy vs other agents, ductal vs mucosal involvement, CIS vs papillary tumors and TURP vs no TURP. RESULTS: Nine studies including 175 patients were identified for inclusion in the systematic review and meta-analysis. All were retrospective case series and most evaluated response to BCG therapy. The pooled global complete response rate for intravesical therapy was 60%(95%CI: 0.48, 0.72), and for prostate 88%(95%CI: 0.81, 0.96). Pre-specified analyses did not demonstrate statistically significant differences between subgroups of interest. CONCLUSIONS: Management of non-invasive prostatic urothelial carcinoma using intravesical therapy yields satisfactory results. Caution should be taken in treating patients with papillary tumors and ductal involvement, as data for these populations is limited. TURP may not improve efficacy, but is required for staging. Current recommendations are based on low quality evidence, and further research is warranted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".