Improving the outcome of patients with muscle invasive urothelial carcinoma of the bladder with neoadjuvant gemcitabine/cisplatin chemotherapy: A single institution experience
Bibliographic record
Abstract
INTRODUCTION: Neoadjuvant cisplatin-based chemotherapy prior to radical cystectomy (RC) for muscle invasive urothelial carcinoma of the bladder improves survival. This study was undertaken to determine the rate of neoadjuvant gemcitabine and cisplatin use prior to RC and to assess its effect on the pathologic response rates and cancer-specific survival (CSS) and overall survival (OS). METHODS: This retrospective chart review examined all patients having a RC between January 1, 2007 and June 30, 2011. We collected patient demographics, pre-treatment clinical stage, type of chemotherapy, post-RC pathologic data and survival data. RESULTS: A total of 251 RC were performed of which 160 were for stage cT2-T4 urothelial carcinoma of the bladder. Of the 160 patients, 91 (57%) received neoadjuvant gemcitabine and cisplatin (GC) and 69 (43%) went straight to RC. Patients receiving neoadjuvant GC had a greater chance of achieving a pathologically lower stage compared to the untreated population: pT0 at 21% vs. 3%; non-invasive cancer at 37% vs. 10%; and organ-confined cancer at 60% vs. 33% (p < 0.001). Survival correlated with pathological stage: ≤pT3a patients had a median OS and CSS of 48.8 and 51.2 months compared to an OS and a CSS in ≥pT3b patients of 21.8 and 28.1 months, respectively (p < 0.0001). CONCLUSIONS: Neoadjuvant chemotherapy for urothelial carcinoma of the bladder is more frequently administered at our institution compared to the published literature. We have found that neoadjuvant chemotherapy increases the rate of down-staging, which is associated with a reduced the risk of death from urothelial carcinoma of the bladder.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".