A Novel Standard or Evolution in Treatment? A Systematic Review with Insights from Single-Center Experience on Robot-Assisted Urachal Excision and Partial Cystectomy for Urachal Pathologies
Bibliographic record
Abstract
Background: Ura-chal pathologies, while rare, pose a malignant transformation risk. RAUEPC is a mini-mally invasive technique with potential benefits, yet evidence remains limited. Methods: A systematic review was conducted in PubMed, Scopus, the Cochrane Library, and Sci-enceDirect (last search: 1 November 2024). Inclusion criteria: studies on RAUEPC for ura-chal pathologies. Exclusion criteria: non-robotic approaches or incomplete data. Risk of bias was assessed using the Newcastle-Ottawa Scale for cohort studies and the JBI Critical Appraisal Checklist for case reports. Descriptive statistics summarized continuous data (means, medians, 95% CIs), and chi-square tests analyzed associations between categori-cal variables. Heterogeneity analysis was infeasible, necessitating narrative synthesis. In-stitutional data (3 cases, 2021–2024) were included for comparison. Results: Forty-four studies (n = 145) met inclusion criteria. Benign lesions constituted 66.2% (95% CI: 59.1–73.3%) and malignant lesions 33.8% (95% CI: 26.7–40.9%). Mean operative time was 177.8 min (95% CI: 96.8–300), blood loss 83.3 mL (95% CI: 50–171), and hospital stay 3.9 days (95% CI: 1–10.9). Complications occurred in 33.3%. Institutional results showed a mean operative time of 85.3 min, blood loss of 216.7 mL, and no recurrences at 10.7 months’ fol-low-up. Discussion: RAUEPC appears to be a feasible and safe approach, showing prom-ising short-term outcomes. Associations between symptoms and diagnostic methods sug-gest its utility. Limitations include small sample sizes and retrospective designs. Registra-tion: PROSPERO: CRD42024597785. Funding: No external funding.
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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 | 0.016 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".