Treatment Outcomes of High-Risk Non-Muscle Invasive Bladder Cancer (HR-NMIBC) in Real-World Evidence (RWE) Studies: Systematic Literature Review (SLR)
Bibliographic record
Abstract
BACKGROUND: To date, there has been limited synthesis of RWE studies in high-risk non-muscle invasive bladder cancer (HR-NMIBC). The objective of this research was to conduct a systematic review of published real-world evidence to better understand the real-world burden and treatment patterns in HR-NMIBC. METHODS: An SLR was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines with the scope defined by the Population, Intervention Comparators, Outcomes, and Study design (PICOS) criteria. EMBASE, MEDLINE, and Cochrane databases (Jan 2015-Jul 2020) were searched, and relevant congress abstracts (Jan 2018-Jul 2020) identified. The final analysis only included studies that enrolled ≥100 patients with HR-NMIBC from the US, Europe, Canada, and Australia. RESULTS: The SLR identified 634 RWE publications in NMIBC, of which 160 studies reported data in HR-NMIBC. The average age of patients in the studies was 71 years, and 79% were males. The rates of BCG intravesical instillations ranged from 3% to 86% (29-95% for induction and 8-83% for maintenance treatment). Five-year outcomes were 17-89% recurrence-free survival (longest survival in patients completing BCG maintenance), 58-89% progression-free survival, 71-96% cancer-specific survival (lowest survival in BCG-unresponsive patients), and 28-90% overall survival (lowest survival in patients who did not receive BCG or instillation therapy). CONCLUSION: BCG treatment rates and survival outcomes in patients with HR-NMIBC vary in the real world, with better survival seen in patients completing maintenance BCG, responding to treatment, and not progressing to muscle-invasive disease. There is a need to better understand the factors associated with BCG use and discontinuation and for an effective treatment that improves outcomes in HR-NMIBC. Generalization of these results is limited by variations in data collection, reporting, and methodologies used across RWE studies.
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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 | 0.025 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.015 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".