The impact of peri-operative chemotherapy for patients with lymph node-positive urothelial cancer.
Bibliographic record
Abstract
388 Background: Patients with locally advanced urothelial cancer with regional lymph node involvement (LN+) have a poor prognosis. Surgical management of these patients is controversial and practice patterns vary. We evaluated the outcomes of patients with LN+ disease treated with pre-operative chemotherapy and cystectomy, cystectomy and post-operative chemotherapy, and chemotherapy alone. Methods: Patients with urothelial cancer with TxN1-3M0 disease treated with chemotherapy in Alberta from 2005 to 2015 were evaluated. Progression-free survival (PFS) and overall survival (OS) were evaluated using Kaplan-Meier analysis. Cox regression analysis was performed to evaluate the impact of age, gender, T stage, and N stage on survival. Results: 184 patients with LN+ disease treated with chemotherapy were evaluable for outcomes; 42 underwent pre-operative chemotherapy (Group A), 92 underwent post-operative chemotherapy (Group B), and 50 received chemotherapy alone (Group C). The median age at diagnosis was 65 years (range 31-89) and most patients (83%) were male. The median follow-up time was 23.2 months. A higher T stage was seen in patients in Group A, while patients in Group C had a higher N stage. The median number of chemotherapy cycles delivered was equal in all arms at 4. Patients in Group A or B had significantly better PFS and OS compared with patients in Group C (Table). When adjusting for age, gender, T stage, and N stage, patients in Group C had significantly lower OS compared with those patients in Group A (HR 1.87, 95% CI 1.09 – 3.18, p=0.02). Conclusions: In this real-world analysis of patients with LN+ urothelial cancer, patient outcomes were improved with surgical resection of disease in combination with pre-operative chemotherapy. After chemotherapy in fit patients with LN+ disease, surgical management is a reasonable consideration. [Table: see text]
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".