Recurrence‐ and progression‐free survival in intermediate‐risk non‐muscle‐invasive bladder cancer: the impact of conditional evaluation and subclassification
Bibliographic record
Abstract
OBJECTIVES: To assess the change in rates of recurrence-free survival (RFS) and progression-free survival (PFS) based on the duration of survival without recurrence or progression among patients with intermediate-risk (IR) non-muscle-invasive bladder cancer (NMIBC), and to examine the predictive factors for recurrence at different time points by assessing conditional RFS and PFS. PARTICIPANTS AND METHODS: A cohort of 602 patients treated with transurethral resection of bladder tumour and histopathologically diagnosed with IR NMIBC was included in this retrospective study. RESULTS: The conditional RFS rate at 1, 2, 3, 4 and 5 years improved with increased duration of RFS; however, the conditional PFS rate did not improve over time. Multivariable analyses showed that recurrent tumour, multiple tumours, tumour size (>3 cm), immediate postoperative instillation of chemotherapy, and administration of BCG were independent predictive factors for recurrence at baseline. The predictive ability of these factors disappeared with increasing recurrence-free survivorship. Subclassification of these patients with IR NMIBC into three groups using clinicopathological factors (recurrent tumour, multiple tumours, tumour size) demonstrated that the high IR group (two factors) had significantly worse RFS than the intermediate (one factor, P < 0.001) and low IR groups (no factor, P = 0.005) at baseline. This subclassification stratified conditional risk of RFS also at 1, 3 and 5 years, which provides the basis for distinct surveillance protocols among patients with IR NMIBC. CONCLUSION: Conditional survival analyses of patients with IR NMIBC demonstrate that RFS changes over time, while PFS does not change. These data support distinct surveillance protocols based on the subclassification of IR NMIBC.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".