Defining statistical cure in patients with T1 high‐grade non‐muscle‐invasive bladder carcinoma treated with BCG immunotherapy
Bibliographic record
Abstract
OBJECTIVE: To assess cure fraction and conditional survival probabilities for oncological outcomes in patients with T1 high-grade (T1HG) non-muscle-invasive bladder carcinoma (NMIBC) treated with adequate Bacillus Calmette-Guérin (BCG) immunotherapy. PATIENTS AND METHODS: This multi-institutional retrospective cohort study included 1403 patients with T1HG NMIBC treated between 2007 and 2020, adhering to modern guidelines including mandatory second transurethral resection of bladder tumour and adequate BCG, defined as at least five of six induction doses and at least two of three maintenance doses. Cure fraction, defined as the proportion of patients who will never experience the event of interest, was calculated for recurrence-free survival (RFS), progression-free survival (PFS), cancer-specific survival (CSS) and overall survival (OS) using a mixture cure model. Conditional survival probabilities over 5 years were also computed. RESULTS: Cure fractions were 41% (95% confidence interval [CI] 36%-46%) for RFS, 71% (95% CI 65%-76%) for PFS, 76% (95% CI 70%-83%) for CSS, and 38% (95% CI 28%-49%) for OS. Patients in very-high-risk subgroups, according to the European Association of Urology risk stratification, showed significantly lower cure fractions, particularly for PFS (61% [95% CI 50%-72%]) and CSS (59% [95% CI 34-84%]). Conditional survival analysis demonstrated that patient prognoses improved over time, with 5-year conditional RFS, PFS, CSS and OS probabilities exceeding 90% for patients who were event-free at 5 years from diagnosis. CONCLUSIONS: We found that BCG immunotherapy achieves a statistical cure in select T1HG NMIBC patients, with avoidance of progression to muscle-invasive bladder carcinoma in approximately 70% of cases. The probability of experiencing any adverse oncological event (recurrence, progression or death) is less than 10% among patients who remain event-free for 5 years.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".