A Review of Neoadjuvant and Adjuvant Chemotherapy for Nonmetastatic Muscle Invasive Bladder Cancer
Bibliographic record
Abstract
INTRODUCTION: Radical cystectomy is the standard treatment for muscle invasive bladder cancer but survival remains poor with radical cystectomy alone. We reviewed the relevant available data on adjuvant and neoadjuvant chemotherapy for bladder cancer. METHODS: We performed a MEDLINE® database literature search to identify original articles and meta-analyses. A key word search was done using the terms urinary bladder neoplasms, cystectomy, chemotherapy, and adjuvant and neoadjuvant therapy. The search was restricted to adults. RESULTS: We studied adjuvant chemotherapy in several prospective, randomized trials that demonstrated improvement in disease-free survival. Many of the trials failed to achieve the target number of accruals, closed early or had major flaws in design. Evidence of the use of neoadjuvant chemotherapy was based on more robust studies that showed a small but significant 5% improvement in overall survival. However this benefit concerned a small set of patients who responded to chemotherapy while another set seemed to fare well with or without neoadjuvant chemotherapy. CONCLUSIONS: Perioperative chemotherapy improves survival in patients with muscle invasive bladder cancer. Molecular predictors of the response to chemotherapy are still in the investigational phase and not yet incorporated into clinical practice. Thus, a risk adapted approach by reserving neoadjuvant chemotherapy for cisplatin eligible patients with high risk disease features may balance the benefits of neoadjuvant chemotherapy while minimizing overtreatment.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| 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 | 0.000 |
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".