A risk-stratified approach to the management of high-grade T1 bladder cancer
Bibliographic record
Abstract
PURPOSE OF REVIEW: A bladder-preserving approach for high-grade nonmuscle invasive bladder cancer that has invaded the lamina propria (T1HG) may result in increased recurrence, progression, and even death from bladder cancer in some patients. Initial radical cystectomy does have increased cancer-specific survival (CSS), but represents significant overtreatment for many patients. An evidence-based, risk-stratified approach is required to select patients for immediate radical cystectomy in order to improve CSS. RECENT FINDINGS: A restaging transurethral resection aids in optimal staging and treatment of T1HG. Intravesical Bacillus Calmette-Guerin induction followed by 3 years of maintenance is the standard adjuvant management. However, when very high-risk (hydronephrosis, abnormal bimanual examination, variant histology, lymphovascular invasion, or residual disease on re-resection, and Bacillus Calmette-Guerin failure or early recurrence) or multiple high-risk factors (concomitant CIS, size >3 cm, multifocality, unfavorable tumor location, extensive lamina propria invasion, and elderly) are present, the risk of progression often outweighs the risk associated with radical cystectomy. In these cases, an immediate radical cystectomy likely provides an improved opportunity for cure compared to a bladder-preserving strategy. SUMMARY: In order to increase the CSS of patients diagnosed with T1HG bladder cancer, an aggressive approach may benefit those with increased risk of progression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".