Management of urachal cancer: A review by the Canadian Urological Association and Genitourinary Medical Oncologists of Canada
Bibliographic record
Abstract
Hamilou et al Management of urachal cancer MethodologyWe performed a search of Pubmed, Embase and Cochrane using the following keywords: urachus cancer, urachus carcinoma, carcinoma of the urachus, cancer of the urachus, urachal cancer.Guidelines from the European Association of Urology (EAU), the National Comprehensive Cancer Network (NCCN) and provincial guidelines from the British Columbia Cancer Agency (BCCA) were reviewed.Only the BCCA guidelines mention urachal cancer.The first draft was written and reviewed by the project leaders (ZH and NB) and disseminated to GUMOC members for a primary review.The updated version was resubmitted to the group as well as key canadian representatives in the fields of urologic oncology, radiation oncology, pathology, and to a patient advocate.Consensus was obtained within the group by the revision of the summary statements until a unanimous agreement was achieved (either by email exchanges or in person discussions at the annual GUMOC meeting).Guidelines for recommendations are described using the World Health Organisation (WHO) modified Oxford Centre for Evidence-based Medicine grading system.The level of evidence was described according to the following: Level 1: systematic review of randomized controlled trials (RCT); Level 2: individual RCT, including low-quality RCT; Level 3: controlled cohort; Level 4: case-control studies or case series; Level 5: expert opinion, mechanism-based reasoning.2
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".