The natural history of secondary muscle-invasive bladder cancer
Bibliographic record
Abstract
BACKGROUND: The management of patients with high-grade non muscle invasive bladder cancer (NMIBC) brings diagnostic and therapeutic challenges. In the current study, we sought to study the natural history of progression to "secondary" muscle-invasive bladder cancer (MIBC)-cancer that developed during follow up of patients presenting with non-muscle invasive bladder cancer (NMIBC). METHODS: Between 1998 and 2008, 760 patients were treated for bladder cancer. Primary MIBC (>=T2) tumors (present upon presentation) were diagnosed in 114 patients. All patients with high-grade NMIBC were treated with intravesical BCG. Mean follow-up was 44 months. RESULTS: Forty patients (6.1%) developed secondary MIBC after a mean period of 21 months from initial diagnosis of bladder cancer. The 2- and 5-year disease-specific survival rates were better for patients with secondary MIBC (90% and 56% compared to 69% and 42% for patients with primary disease, p=0.03). The Kaplan-Meier curves of the two groups were parallel but displaced by approximately 2 years. CONCLUSION: In the current series, MIBC progression occurred among initially presenting patients with NMIBC in 6.1%. In most patients, the initial diagnosis of NMIBC is correct and muscle invasion occurs after a mean period of about 2 years. This supports a non-radical approach in patients with high-grade T1, Ta or Tis. Meticulous follow-up with liberal biopsy of any suspicious lesion may provide early diagnosis of invasive disease.
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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 | 0.003 |
| 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) | 0.001 | 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".