MP57-10 BENEFITS AND COSTS OF ALTERNATIVE GUIDELINES FOR SURVEILLANCE OF LOW-GRADE (LG) NON-MUSCLE INVASIVE BLADDER CANCER (NMIBC)
Bibliographic record
Abstract
You have accessJournal of UrologyHealth Services Research: Value of Care: Cost and Outcomes II (MP57)1 May 2024MP57-10 BENEFITS AND COSTS OF ALTERNATIVE GUIDELINES FOR SURVEILLANCE OF LOW-GRADE (LG) NON-MUSCLE INVASIVE BLADDER CANCER (NMIBC) Zhuo T. Su, Katherine Mahon, Michael Rezaee, Sunil Patel, Jeffrey Townsend, and Max Kates Zhuo T. SuZhuo T. Su , Katherine MahonKatherine Mahon , Michael RezaeeMichael Rezaee , Sunil PatelSunil Patel , Jeffrey TownsendJeffrey Townsend , and Max KatesMax Kates View All Author Informationhttps://doi.org/10.1097/01.JU.0001009420.83948.eb.10AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: How to reduce the high costs of bladder cancer care without compromising clinical outcomes remains understudied. We used simulations to compare the benefits and costs of major guidelines for surveillance of LG NMIBC. METHODS: We developed a Monte-Carlo simulation to model the 10-year outcomes and costs of 5 guidelines for surveillance of a cohort of 10,000 patients diagnosed with LGTa or LGT1 NMIBC at age 70 years: the American Urological Association (AUA), National Comprehensive Cancer Network (NCCN), European Association of Urology (EAU), Canadian Urological Association (CUA), and National Institute for Health and Care Excellence (NICE). We assessed separately the most intense (denoted as AUAi and CUAi) and relaxed (AUAr and CUAr) surveillance regimens within the range of surveillance frequencies allowed by the AUA and CUA guidelines. RESULTS: For LGTa, 10-year cumulative incidence (CI) of muscle invasive bladder cancer (MIBC) ranged from 0.3% (CUAi) to 0.6% (NICE); cancer-specific survival (CSS) and overall survival (OS) were both nearly identical across guidelines (Table 1). For LGT1, AUAi and CUAi both led to the best MIBC CI (4.1%), CSS (71.8%), and OS (98.2%), while AUAr had the worst MIBC CI (5.5%; absolute difference 1.4%), CSS (71.4%; 0.4%), and OS (97.4%; 0.8%). In cost-effectiveness analysis, NICE was the optimal option for LGTa as it obtained similar health utilities versus the other surveillance regimens but incurred the lowest costs (Table 2). For LGT1, more stringent regimens such as CUAi achieved higher health utilities but also had high incremental costs compared to less stringent regimens and were not cost-effective per conventional cost-effectiveness thresholds. CONCLUSIONS: For LGTa, relatively less stringent surveillance regimens led to cost savings without markedly affecting oncological outcomes and were cost-effective. For LGT1, more stringent surveillance regimens achieved better oncological outcomes but incurred much higher costs to be deemed cost-effective versus less stringent regimens. Source of Funding: None © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e941 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Zhuo T. Su More articles by this author Katherine Mahon More articles by this author Michael Rezaee More articles by this author Sunil Patel More articles by this author Jeffrey Townsend More articles by this author Max Kates More articles by this author Expand All 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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.008 | 0.001 |
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".