PD08-02 THE BLADDER UTILITY SYMPTOM SCALE (UTILITY): A NOVEL TOOL TO MEASURE UTILITIES AND QUALITY OF LIFE IN BLADDER CANCER PATIENTS
Bibliographic record
Abstract
You have accessJournal of UrologyHealth Services Research: Value of Care: Cost and Outcomes I (PD08)1 May 2024PD08-02 THE BLADDER UTILITY SYMPTOM SCALE (UTILITY): A NOVEL TOOL TO MEASURE UTILITIES AND QUALITY OF LIFE IN BLADDER CANCER PATIENTS Girish S. Kulkarni, Nathan Perlis, Douglas Cheung, Karen E. Bremner, Mia Papasideris, Katherine Lajkosz, Nicholas Power, Robert K. Nam, and George Tomlinson Girish S. KulkarniGirish S. Kulkarni , Nathan PerlisNathan Perlis , Douglas CheungDouglas Cheung , Karen E. BremnerKaren E. Bremner , Mia PapasiderisMia Papasideris , Katherine LajkoszKatherine Lajkosz , Nicholas PowerNicholas Power , Robert K. NamRobert K. Nam , and George TomlinsonGeorge Tomlinson View All Author Informationhttps://doi.org/10.1097/01.JU.0001008576.33217.96.02AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Bladder cancer (BCa) and its treatments have significant impacts on patient quality of life (QOL) and decision-making. To facilitate comparative effectiveness research, a BCa specific tool to measure both quality of life and utilities is required. We previously created and validated the Bladder Utility Symptom Scale (BUSS)-Psychometric (P), a 10-item multiple-choice questionnaire to measure QOL in all phases of BCa care. Our objective was to create two distinct algorithms to calculate utilities from BUSS-P responses. METHODS: We conducted in-person interviews with 200 BCa patients and 200 members of the general public. Purposeful sampling was used to ensure proportionate numbers of non-muscle invasive (NMIBC), muscle invasive (MIBC) and metastatic BCa patients. The general public sample was recruited proportionate to national age, sex, and income distributions. Each respondent provided time tradeoff (TTO) utilities for 12 randomly-generated health state scenarios based on the BUSS-P attributes. Bayesian generalized linear multilevel models were used to estimate the impact of each of the 10 BUSS-P attributes to utility which was bound by 0 and 1. Pearson correlation coefficients were calculated between observed and expected model values. Two algorithms to calculate utilities from BUSS-P responses were then generated – one derived from BCa patients and one from the general public. RESULTS: Of 400 participants, 322 completed the TTO exercises with adequate comprehension. Of the BCa patients, 70 were NMIBC, 53 MIBC and 32 metastatic. A total of 3,288 randomly generated, unique BUSS-P health state valuations were obtained. The final model had a weighted correlation coefficient between predicted and observed utilities of 0.733 and 0.734 in the community and patient groups, respectively. A final table of weights for each response level of each question was created for final utility calculation. In an exploratory analysis of patients' own BUSS-Ucresponses, discrimination of utilities across health states was observed with mean (SD) utilities in NMIBC, cystectomy and minimally symptomatic metastatic patients at 0.897 (0.099), 0.831 (0.109) and 0.825 (0.157), respectively. CONCLUSIONS: The BUSS-P is the first instrument that provides utilities for BCa derived from both BCa patients and the general public. Grounded in robust TTO methodology, utilities in all phases of BCa care can be measured for use in comparative effectiveness research, cost-effectiveness and decision modeling and policy work. Source of Funding: Canadian Institutes for Health Research and the Canadian Cancer Society © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e173 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Girish S. Kulkarni More articles by this author Nathan Perlis More articles by this author Douglas Cheung More articles by this author Karen E. Bremner More articles by this author Mia Papasideris More articles by this author Katherine Lajkosz More articles by this author Nicholas Power More articles by this author Robert K. Nam More articles by this author George Tomlinson More articles by this author Expand All Advertisement PDF downloadLoading ...
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| 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 teacher head, 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".