Patient Preferences for Adjuvant Treatment in Muscle-Invasive Urothelial Carcinoma: A Multi-Country Discrete Choice Experiment
Bibliographic record
Abstract
Purpose: The evolving treatment landscape in muscle-invasive urothelial carcinoma creates challenges for clinicians and patients in selecting the most appropriate therapy. Here, we aimed to understand adjuvant treatment preferences among patients with muscle-invasive urothelial carcinoma who underwent radical resection, including tradeoffs between efficacy outcomes and toxicity risks. Patients and Methods: An observational, cross-sectional study utilizing a discrete choice experiment was conducted across the United States, United Kingdom, Canada, France, and Germany via a web-based survey. Patients ≥18 years of age who self-reported as having been diagnosed with muscle-invasive urothelial carcinoma were included. Patients indicated their preferences between hypothetical treatment profiles varying in eight attributes relating to efficacy, regimen, and side effects. Preference weights were estimated using hierarchical Bayesian logistic regression; relative attribute importance estimates were calculated. Results: Overall, 207 patients were included (age ≥56 years, 65.7%; male, 54.1%). Patients chose adjuvant treatment 91.2% of the time vs no treatment. Prolonging overall survival from 25 to 78 months was most important, followed by reducing serious side effect risks. Increasing disease-free survival from 12 to 24 months was more important than decreasing risks of fatigue from 54% to 15% and nausea from 53% to 7%. Treatment with the shortest dosing regimen was more important for patients who received neoadjuvant chemotherapy vs patients who did not receive neoadjuvant chemotherapy; prolonging overall survival was more important than reducing the risk of a serious side effect in non-US patients; the opposite was found in the United States. Conclusion: Patients with muscle-invasive urothelial carcinoma who underwent radical resection preferred adjuvant treatment over no treatment regardless of side effects. Patients prioritized overall survival improvements followed by a reduced side effect profile.
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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 | 0.018 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".