Risk‐adapted management of low‐grade bladder tumours: recommendations from the International Bladder Cancer Group (IBCG)
Bibliographic record
Abstract
OBJECTIVE: To provide a contemporary update and recommendations for the diagnosis and management of low-grade non-muscle-invasive bladder cancer (BCa) based on current literature and expert consensus of the International Bladder Cancer Group. METHODS: We reviewed published trials, guidelines, meta-analyses and reviews (up to March 2019) and provide recommendations on baseline evaluations, treatment, endpoints, study design and surveillance protocols. RESULTS: Low-grade Ta BCa poses minimal risk to patients in terms of progression and disease-specific survival. Thus, to minimize patient morbidity, this entity should be managed appropriately. After initial diagnosis of low-grade Ta tumour, subsequent stable, low-grade-appearing recurrences can be managed conservatively with office cystoscopy and fulguration or even followed using an active surveillance protocol. Intravesical therapy other than single-dose peri-operative chemotherapy instillation should be used judiciously, and only after assigning appropriate risk points. Routine use of urinary cytology - other than at initial risk stratification, or for patients on active surveillance without therapy - is not recommended; and surveillance cystoscopy may be discontinued after 5 years. Clinical studies in this group of patients should focus on recurrence rates, and time to recurrence, rather than progression events. CONCLUSIONS: The International Bladder Cancer Group has developed formal recommendations regarding the diagnosis, treatment and surveillance of low-grade non-muscle-invasive BCa to minimize morbidity and encourage uniformity among studies in this disease.
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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 | 0.024 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".