Multi-institutional quality-of-care initiative for nonmetastatic, muscle-invasive, transitional cell carcinoma of the bladder: Phase I.
Bibliographic record
Abstract
240 Background: Evidence supports multimodality treatment for muscle invasive bladder cancer [MIBC] with the strongest evidence (level 1) existing for cisplatin-based neoadjuvant chemotherapy. Although reflected in guidelines for the management of MIBC, little is known about the variation of actual practice patterns among academic institutions. We thus evaluated treatment variation among 14 academic centers in the management of patients with MIBC. Methods: Retrospective data were collected for centralized analysis. All patients who underwent radical cystectomy for clinical T2-4 N0M0 MIBC from 2003–2008 were eligible for inclusion. Specific endpoints for analysis included: rates of neoadjuvant and adjuvant therapy, cisplatin use, number of cycles and rates of pelvic lymphadenectomy. Results: 14 institutions participated and data on 4,541 patients who met inclusion criteria were tabulated. Overall 34% of patients received perioperative chemotherapy. The overall use of neoadjuvant and adjuvant therapy was 12% and 22%, respectively. In a subset analysis of those patients with specific chemotherapy agent information provided (n=3,120), 59% of patients managed with perioperative chemotherapy received cisplatin. Of those who received treatment in the neoadjuvant setting, cisplatinum was received in 65% of cases (supported by level 1 evidence). 80% of patients who received perioperative chemotherapy received at least 3 cycles. At radical cystectomy 95% of patients received a bilateral PLND. Conclusions: In this cohort of academic North American centers, 66% of potentially eligible bladder cancer patients undergoing radical cystectomy did not receive perioperative chemotherapy. Only 12% of patients received neoadjuvant chemotherapy, and 35% of those patients received a non-cisplatin based regimens. Despite level 1 evidence that cisplatin based neoadjuvant chemotherapy is associated with a survival advantage, only a small percentage of eligible patients undergoing radical cystectomy for muscle invasive, resectable disease receive combined treatment. Further study is needed clarify specific reasons for the treatment variation observed in academic centers. No significant financial relationships to disclose.
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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 | 0.026 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".