Active Surveillance in Non-Muscle Invasive Bladder Cancer: A Systematic Review
Bibliographic record
Abstract
Bladder cancer is the ninth most common cancer globally, with most cases classified as non-muscle-invasive bladder cancer (NMIBC). While transurethral resection of the bladder tumor (TURBT) remains the gold-standard treatment, its complications, high recurrence rates, and economic burden have prompted interest in alternative strategies like active surveillance (AS) for low-grade and low-grade NMBIC recurrences. AS minimizes surgical interventions and patient burden, but lacks standardized protocols for inclusion criteria and follow-up schedules. Most studies suggest intensive monitoring during the first year, with criteria often based on tumor size, number, and grade. ACQUISITION OF EVIDENCE: A comprehensive literature search was conducted in December 2024 using Pubmed, Cochrane, and Trip databases to identify studies on AS for low-grade NMBIC recurrences. Only English studies were included, with Boolean operators used to refine the search. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the Population, Intervention, Comparison and Outcomes (PICO) selection criteria were followed. The Newcastle-Ottawa quality assessment scale was used to analyze the quality of the included studies. EVIDENCE SYNTHESIS: This systematic review included 11 studies evaluating AS for NMIBC. Early studies, such demonstrated AS as a feasible alternative to TURBT, with low progression rates. Subsequent research confirmed its safety in selected patients, with tumor growth and positive cytology being the main reasons for intervention. More recent investigations, further supported AS as a viable strategy, highlighting the low risk of stage and grade progression and its potential to reduce surgical interventions. CONCLUSIONS: AS may be considered an alternative approach for low-risk NMIBC recurrences. However, there is need for prospective studies and personalized approaches to optimize AS, addressing follow-up strategies, inclusion criteria and progression thresholds.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".