Urachal Carcinomas: A Comprehensive Systematic Review and Meta-analysis
Bibliographic record
Abstract
OBJECTIVE: This systematic review and meta-analysis aim to consolidate current evidence on the diagnosis, epidemiology, and treatment of urachal carcinoma, a rare malignancy with limited data. MATERIALS AND METHODS: A systematic search of PubMed/MEDLINE was conducted up to September 2024 to identify studies involving patients with urachal carcinoma, reporting clinical epidemiological characteristics, diagnostic strategies, histopathological findings, tumor staging, treatment modalities, and oncological outcomes. Extracted data were systematically synthesized, and statistical analyses, including a single-arm meta-analysis, were performed to comprehensively evaluate oncological outcomes. RESULTS: Our study includes 1,901 cases of urachal carcinoma from 50 studies. The findings support the oncologic advantage of en-bloc resection with umbilectomy in localized disease, demonstrating improved survival outcomes and reduced recurrence rates. In the adjuvant setting, those receiving cisplatin-based therapy presented the best response, with 65.73% with no disease progression; similarly, in the metastatic disease, cisplatin-based regimens seem to have better responses in metastatic disease. The single-arm meta-analysis estimated a 5-year overall survival rate of 51% (95% CI: 0.49-0.54). Tumor recurrence was documented in 35% of cases (95% CI: 0.25-0.45), with local recurrence occurring in 28% (95% CI: 0.18-0.38), with the average time to recurrence of 27.6 months. CONCLUSION: Our study provides the most comprehensive review of urachal carcinoma to date, providing evidence to guide clinical decisions. It underscores the oncologic benefits of en-bloc resection with umbilectomy and specific chemotherapeutic regimens. Emerging alternative therapies also show potential, highlighting the need for further research to optimize patient outcomes.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.038 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".