MP12-09 QUALITY OF LIFE AND HEALTH STATE UTILITIES IN BLADDER CANCER PATIENTS ACROSS THE CARE TRAJECTORY
Bibliographic record
Abstract
You have accessJournal of UrologyHealth Services Research: Practice Patterns, Quality of Life and Shared Decision Making I (MP12)1 May 2024MP12-09 QUALITY OF LIFE AND HEALTH STATE UTILITIES IN BLADDER CANCER PATIENTS ACROSS THE CARE TRAJECTORY Mia Papasideris, Karen E. Bremner, Douglas C. Cheung, Peter E. Black, Wassim Kassouf, William L. Wong, and Girish S. Kulkarni Mia PapasiderisMia Papasideris , Karen E. BremnerKaren E. Bremner , Douglas C. CheungDouglas C. Cheung , Peter E. BlackPeter E. Black , Wassim KassoufWassim Kassouf , William L. WongWilliam L. Wong , and Girish S. KulkarniGirish S. Kulkarni View All Author Informationhttps://doi.org/10.1097/01.JU.0001009376.16371.fb.09AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Utility is a preference-based measure of health-related quality of life (HRQOL) used in cost-effectiveness and decision models. There are few robust utility data for bladder cancer (BCa) patients, and none span the entire care trajectory. The objective of this study was to measure utilities and HRQOL in BCa patients at all phases of care. METHODS: In this prospective multi-centre study, we identified 15 BCa health states from Diagnosis to Third Line Metastatic disease based on patients' expected pathways (Table 1). Consenting BCa patients attending outpatient urology, medical oncology and palliative care clinics at 3 tertiary care centres in 3 Canadian cities were allocated to a health state based on medical chart review. They were re-evaluated every 1 to 6 months for up to 2 years for transition to a second health state. At baseline and transition, patients completed 3 validated published utility instruments: our internally developed Bladder Utility Symptom Scale (BUSS), EQ-5D-5L, and the Quality of Life Utility-Core 10 Dimensions (QLU-C10D; derived from the EORTC QLQ-C30). We report descriptive statistics across instruments and health states. RESULTS: A total of 371 patients (mean age 69 years, 75% male) completed at least 1 utility instrument, and 358 completed all 3 at least once, with 411 health state observations. Mean utilities were higher at Diagnosis (BUSS: 0.87; EQ-5D-5L: 0.86; QLU-C10D: 0.81) and NMIBC Surveillance (BUSS: 0.91; EQ-5D-5L: 0.86; QLU-C10D: 0.81) than during treatment (Table 1). The lowest mean utilities were with radical cystectomy, at 0.65 (BUSS), 0.76 (EQ-5D-5L), and 0.55 (QLU-C10D) for 34 patients. EORTC QLQ-C30 function and symptom scales mirrored trends in utilities. Fatigue, insomnia, and gastrointestinal symptoms were common during treatment states (Figure 1). CONCLUSIONS: We generated the first reference standard set of utilities for all phases of BCa care. Patients reported large variability in HRQOL outcomes over the care pathway. These utilities can inform cost-effectiveness and comparative effectiveness studies. Download PPT Source of Funding: Canadian Institutes of Health Research Project Grant awards, 390221 (Bridge funding) and PJT 173386 © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e203 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Mia Papasideris More articles by this author Karen E. Bremner More articles by this author Douglas C. Cheung More articles by this author Peter E. Black More articles by this author Wassim Kassouf More articles by this author William L. Wong More articles by this author Girish S. Kulkarni 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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.072 | 0.010 |
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".