Management of Localized Muscle-Invasive Bladder Cancer from a Multidisciplinary Perspective: Current Position of the Spanish Oncology Genitourinary (SOGUG) Working Group
Bibliographic record
Abstract
This review presents challenges and recommendations on different aspects related to the management of patients with localized muscle-invasive bladder cancer (MIBC), which were discussed by a group of experts of a Spanish Oncology Genitourinary (SOGUG) Working Group within the framework of the Genitourinary Alliance project (12GU). It is necessary to clearly define which patients are candidates for radical cystectomy and which are candidates for undergoing bladder-sparing procedures. In older patients, it is necessary to include a geriatric assessment and evaluation of comorbidities. The pathological report should include a classification of the histopathological variant of MIBC, particularly the identification of subtypes with prognostic, molecular and therapeutic implications. Improvement of clinical staging, better definition of prognostic groups based on molecular subtypes, and identification of biomarkers potentially associated with maximum benefit from neoadjuvant chemotherapy are areas for further research. A current challenge in the management of MIBC is improving the selection of patients likely to be candidates for immunotherapy with checkpoint inhibitors in the neoadjuvant setting. Optimization of FDG-PET/CT reliability in staging of MIBC and the selection of patients is necessary, as well as the design of prospective studies aimed to compare the value of different imaging techniques in parallel.
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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 | 0.002 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".