Active surveillance for non-muscle-invasive bladder cancer: fallacy or opportunity?
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review aims to analyze the current place of active surveillance (AS) in non-muscle-invasive bladder cancer (NMIBC). RECENT FINDINGS: A growing body of evidence suggests that AS protocols for pTa low-grade (TaLG) NMIBC are safe and feasible. However, current guidelines have not implemented AS due to a lack of high-quality data. Available studies included pTa tumors, with only one study excluding pT1-NMIBC. Inclusion/exclusion criteria were heterogeneously defined based on tumor volume, number of tumors, carcinoma in situ (CIS), or high-grade (HG) NMIBC. Tumor volume <10 mm and <5 lesions were used as cut-offs. Positive urinary cytology (UC) or cancer-related symptoms precluded inclusion. Surveillance within the first year consisted of quarterly cystoscopy. AS stopped upon the presence of cancer-related symptoms, change in tumor morphology, positive UC, or patient's request. With a median time on AS of 16 months, two-thirds of the patients failed AS. Progression to muscle-invasive bladder cancer (MIBC) was rare and occurred only in patients with pT1-NIMBC at inclusion. SUMMARY: AS in NMIBC is an attractive concept in the era of personalized medicine, but strong evidence is still awaited. A more precise definition of patient inclusion, follow-up, and failure criteria is required to improve its implementation in daily clinical practice.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".