Are we undertreating invasive bladder cancer? Optimizing outcomes in a high-risk disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: To highlight the important issues that may improve patient outcomes in the setting of invasive bladder cancer. RECENT FINDINGS: Although approximately 80% of patients with bladder cancer present with disease confined to the mucosa which can be treated locally, progression to muscle-invasive bladder carcinoma (MIBC) is associated with adverse outcomes. Stage and grade have traditionally been used to predict progression. European Organization for Research and Treatment of Cancer (EORTC) and Club Urológico Español de Tratamiento Oncológico (CUETO) have designed prognostic models that further refine risk assessment. Recent attempts to integrate molecular biomarkers may further improve these models. Treating appropriate patients earlier with radical cystectomy offers the hope of decreasing the extent of disease at the time of surgery. In patients with MIBC, neoadjuvant chemotherapy has been shown to improve patient outcome. Selecting appropriate patients remains a challenge. Preoperative models to predict risk of lymph node-positive disease and preoperative imaging with fluorodeoxyglucose positron emission tomography or MRI have been shown to be useful in that regard. Multidisciplinary care offers better patient support and collaboration during the treatment phase and improves quality of life. SUMMARY: Improved outcomes in localized bladder cancer requires an integrated approach including better identification of high-risk disease, earlier use of cystectomy, broader use of chemotherapy and the availability of a dedicated multidisciplinary team.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".