Canadian Urological Association guideline on the management of non-muscle invasive bladder cancer
Bibliographic record
Abstract
Prognostic factors for recurrence and progression 5.The most important prognostic factors for recurrence and progression of non-muscleinvasive bladder cancer (NMIBC) are stage and grade (LE 2).All patients with bladder cancer should be properly staged and, specifically for NMIBC, reporting grade is paramount for further management decisions (LE 2; strong recommendation).6.Other prognostic factors are age >70yr, large tumour size (≥3cm), multiple tumours, the presence of concomitant carcinoma in situ (CIS), extensive invasion of the lamina propria, prior recurrence rates > 1 per year and status at first assessment after transurethral resection of the bladder tumour (TURBT) (LE 2), as well as lymphovascular invasion (LVI) (LE 3). 7. Aggressive histological variants such as micropapillary, plasmacytoid and sarcomatoid are associated with increased risk of under-staging and progression (LE 3).Pathological review, preferably by a dedicated uro-pathologist, should be considered in settings where variant histology is suspected or atypical tumours are seen during TURBT (e.g., sessile mass) (LE 3; weak recommendation). Risk stratification8. All patients with NMIBC should be stratified according to the risk of both recurrence and progression for adequate patient counselling and treatment planning (LE 2; strong recommendation).The modified CUA risk stratification system is a suitable tool for this purpose. CUAJ -CUA GuidelineBhindi et al Guideline: NMIBC management 4
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 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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".