Maintenance bacillus Calmette‐Guérin in high‐risk nonmuscle‐invasive bladder cancer
Bibliographic record
Abstract
BACKGROUND: Intravesical bacillus Calmette-Guérin (BCG) immunotherapy is effective in preventing recurrence and progression in nonmuscle-invasive bladder cancer but the dosing schedule and duration of treatment remain empirical. The outcome of BCG therapy was prospectively evaluated on patients according to the number of maintenance cycles received. METHODS: Between 1997 and 2002, 111 patients with nonmuscle-invasive bladder cancer at high risk of recurrence and progression underwent transurethral resection followed by intravesical instillations of BCG. After an induction treatment of 6 weekly instillations, patients were scheduled to receive a 3-weekly maintenance treatment at the 3rd, 6th, 12th, 18th, 24th, 30th, and 36th month. At each visit a clinical assessment was obtained. RESULTS: Over a median clinical follow-up of 31 months, 43% of patients experienced recurrence and 8.1% progressed to muscle-invasive disease or metastasis. Only 1 patient received all scheduled instillations. Patients who received at least 3 maintenance BCG cycles had a significantly reduced risk of recurrence (hazard ratio [HR] = 0.23, P = .0064, adjusted for gender, age, and stage) compared with patients receiving only induction BCG. Twelve months after the end of maintenance, the estimated Kaplan-Meier recurrence-free survival was 89% for patients who received at least 3 maintenance BCG cycles, 67% for those who received 2 maintenance BCG cycles, and 41% for those who received only the induction BCG or 1 maintenance cycle (P = .0003). CONCLUSIONS: The results of this study suggest that a minimum of 3 cycles of maintenance BCG is required to significantly reduce the recurrence rate. It also suggests that more cycles may result in further improvements but the benefits may be outweighed by increasing side effects in some patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".