485 MULTI-INSTITUTIONAL QUALITY OF CARE INITIATIVE FOR NON-METASTATIC MUSCLE-INVASIVE TRANSITIONAL CELL CARCINOMA OF THE BLADDER: PHASE 1
Bibliographic record
Abstract
You have accessJournal of UrologyUrothelial Cancer: Medical & Surgical Therapy1 Apr 2011485 MULTI-INSTITUTIONAL QUALITY OF CARE INITIATIVE FOR NON-METASTATIC MUSCLE-INVASIVE TRANSITIONAL CELL CARCINOMA OF THE BLADDER: PHASE 1 Andrew Feifer, Jennifer Taylor, Marwan Shouery, Caroline Savage, Gary Steinberg, Walter M. Stadler, Joel DeCastro, Seth Lerner, Guilherme Godoy, Yair Lotan, Adam S. Feldman, Sarah Psutka, Wassim Kassouf, Faysal Yafi, Alexander Zlotta, Peter Black, Mark Schoenberg, Robert Grubb, Michael O'Donnell, Henry Rosevear, Andrew Stephenson, Amit Patel, Cheryl Lee, Alon Weizer, Dean Bajorin, Matthew Milowsky, Guido Dalbagni, Ethan Basch, and Bernard Bochner Andrew FeiferAndrew Feifer New York, NY More articles by this author , Jennifer TaylorJennifer Taylor New York, NY More articles by this author , Marwan ShoueryMarwan Shouery New York, NY More articles by this author , Caroline SavageCaroline Savage New York, NY More articles by this author , Gary SteinbergGary Steinberg Chicago, IL More articles by this author , Walter M. StadlerWalter M. Stadler Chicago, IL More articles by this author , Joel DeCastroJoel DeCastro Chicago, IL More articles by this author , Seth LernerSeth Lerner Houston, TX More articles by this author , Guilherme GodoyGuilherme Godoy Houston, TX More articles by this author , Yair LotanYair Lotan Dallas, TX More articles by this author , Adam S. FeldmanAdam S. Feldman Boston, MA More articles by this author , Sarah PsutkaSarah Psutka Boston, MA More articles by this author , Wassim KassoufWassim Kassouf Montreal, Canada More articles by this author , Faysal YafiFaysal Yafi Montreal, Canada More articles by this author , Alexander ZlottaAlexander Zlotta Toronto, Canada More articles by this author , Peter BlackPeter Black Vancouver, Canada More articles by this author , Mark SchoenbergMark Schoenberg Baltimore, MD More articles by this author , Robert GrubbRobert Grubb St. Louis, MO More articles by this author , Michael O'DonnellMichael O'Donnell Iowa City, United Kingdom More articles by this author , Henry RosevearHenry Rosevear Iowa City, IA More articles by this author , Andrew StephensonAndrew Stephenson Cleveland, OH More articles by this author , Amit PatelAmit Patel Cleveland, OH More articles by this author , Cheryl LeeCheryl Lee Ann Arbor, MI More articles by this author , Alon WeizerAlon Weizer Ann Arbor, MI More articles by this author , Dean BajorinDean Bajorin New York, NY More articles by this author , Matthew MilowskyMatthew Milowsky New York, NY More articles by this author , Guido DalbagniGuido Dalbagni New York, NY More articles by this author , Ethan BaschEthan Basch New York, NY More articles by this author , and Bernard BochnerBernard Bochner New York, NY More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2011.02.580AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Evidence supports a multimodality treatment approach 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 from all institutions. All patients that underwent radical cystectomy for clinical T2-4 N0M0MIBC from 2003−2008 were eligible for inclusion. Specific endpoints for analysis included: rates of neoadjuvant and adjuvant therapy, cisplatin utilization, number of cycles and rates of pelvic lymphadenectomy. RESULTS 14 institutions participated and data on 4541 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=3120), 59% of patients managed with perioperative chemotherapy received a cisplatin-based regimen. Of those who received treatment in the neoadjuvant setting, cisplatin 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 T2-4 N0M0MIBC 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 regimen. 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 to clarify specific reasons for the treatment variation observed in academic centers. © 2011 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 185Issue 4SApril 2011Page: e198 Advertisement Copyright & Permissions© 2011 by American Urological Association Education and Research, Inc.MetricsAuthor Information Andrew Feifer New York, NY More articles by this author Jennifer Taylor New York, NY More articles by this author Marwan Shouery New York, NY More articles by this author Caroline Savage New York, NY More articles by this author Gary Steinberg Chicago, IL More articles by this author Walter M. Stadler Chicago, IL More articles by this author Joel DeCastro Chicago, IL More articles by this author Seth Lerner Houston, TX More articles by this author Guilherme Godoy Houston, TX More articles by this author Yair Lotan Dallas, TX More articles by this author Adam S. Feldman Boston, MA More articles by this author Sarah Psutka Boston, MA More articles by this author Wassim Kassouf Montreal, Canada More articles by this author Faysal Yafi Montreal, Canada More articles by this author Alexander Zlotta Toronto, Canada More articles by this author Peter Black Vancouver, Canada More articles by this author Mark Schoenberg Baltimore, MD More articles by this author Robert Grubb St. Louis, MO More articles by this author Michael O'Donnell Iowa City, United Kingdom More articles by this author Henry Rosevear Iowa City, IA More articles by this author Andrew Stephenson Cleveland, OH More articles by this author Amit Patel Cleveland, OH More articles by this author Cheryl Lee Ann Arbor, MI More articles by this author Alon Weizer Ann Arbor, MI More articles by this author Dean Bajorin New York, NY More articles by this author Matthew Milowsky New York, NY More articles by this author Guido Dalbagni New York, NY More articles by this author Ethan Basch New York, NY More articles by this author Bernard Bochner New York, NY More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...
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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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.007 |
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".