Bladder-sparing protocols in the treatment of muscle-invasive bladder cancer
Bibliographic record
Abstract
Bladder-sparing protocols (BSP) have been gaining widespread popularity as an attractive alternative to radical cystectomy (RC) for muscle-invasive bladder cancer. Unimodal therapies are inferior to multimodal regimens. The most promising regimen is trimodal therapy (TMT), which is a combination of maximal transurethral resection of bladder tumor (TURBT), radiotherapy, and chemotherapy. In appropriately selected patients (low volume unifocal T2 disease, complete TURBT, no hydronephrosis and no carcinoma-in-situ), comparable oncological outcomes to RC have been reported in large retrospective studies, with a potential improvement in overall quality of life (QOL). TMT also offers the possibility for definitive therapy for patients who are not surgically fit to undergo RC. Routine biopsy of previous tumor resection is recommended to assess response. Prompt salvage RC is required in non-responders and for recurrent muscle-invasive disease, while non-muscle-invasive recurrence can be managed conservatively with TURBT +/- intravesical BCG. Long-term follow-up consisting of routine cystoscopy, urine cytology, and cross-section imaging is required. Further studies are warranted to better define the role of neoadjuvant or adjuvant chemotherapy in the setting of TMT. Finally, future research on predictive markers of response to TMT and on the integration of immunotherapy in bladder sparing protocols is ongoing and is highly promising.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.004 | 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".