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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| 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".