Estimation of the incidence of urachal cancer: A systematic review and meta-analysis of registry-based studies
Bibliographic record
Abstract
BACKGROUND: Urachal cancer (UrC) is a rare disease with limited availability of representative incidence and clinical data. Although, the prevalence is accounting for less than 1% of bladder tumors, the 5-year survival rate is around only 50% for patients with resectable tumors, and even worse for patients with metastatic disease. Due to the lack of comprehensive prospective studies, our current knowledge of UrC is still limited. OBJECTIVE: The present study aimed to summarize the available registry-based studies with unselected UrC patients to evaluate its incidence and clinicopathological characteristics. MATERIAL AND METHODS: We conducted a systematic literature search of registry-based UrC publications on the 15th of May 2023 in 5 databases, which identified 4,748 publications. After duplicate removal and selection by 2 independent investigators, 6 publications proved to be appropriate for the final meta-analysis. Estimated incidence and clinicopathological parameters were extracted. RESULTS: Estimated incidence ranged between 0.022 and 0.060/ 100.000 person-years, with the highest occurrence in Japan and the lowest in Canada, while the random effect model calculated an overall incidence rate of 0.04 (95%CI: 0.03-0.05) 100.000 person-years. The median age at first diagnosis was 60 years (range: 58-64). The female to male ratio was 2:3. Lymph node or distant metastases were present in 9% and 14% of patients. The predominant tumour type was adenocarcinoma (86%) followed by urothelial carcinoma (12%) and squamous cell carcinoma (2%). The 5-year survival rate was 51.0% with 95%CI: 45.2-57.4. CONCLUSIONS: Our study provides an up-to-date comparison of estimated incidence rates between 6 countries of 3 continents based on rigorously selected registry-based studies. The results suggest low incidence rates for UrC with considerable geographic differences. The present meta-analysis provides unbiased registry-based data on the incidence, clinicopathological parameters and survival of UrC.
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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 | 0.020 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.033 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".