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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".