Discrete-Choice Experiment to Understand the Preferences of Patients with Hormone-Sensitive Prostate Cancer in the USA, Canada, and the UK
Bibliographic record
Abstract
BACKGROUND: Treatment options for patients with metastatic hormone-sensitive prostate cancer (mHSPC) have broadened, and treatment decisions can have a long-lasting impact on patients' quality of life. Data on patient preferences can improve therapeutic decision-making by helping physicians suggest treatments that align with patients' values and needs. OBJECTIVE: This study aims to quantify patient preferences for attributes of chemohormonal therapies among patients with mHSPC in the USA, Canada, and the UK. METHODS: A discrete-choice experiment survey instrument was developed and administered to patients with high- and very-high-risk localized prostate cancer and mHSPC. Patients chose between baseline androgen-deprivation therapy (ADT) alone and experimentally designed, hypothetical treatment alternatives representing chemohormonal therapies. Choices were analyzed using logit models to derive the relative importance of attributes for each country and to evaluate differences and similarities among patients across countries. RESULTS: A total of 550 respondents completed the survey (USA, 200; Canada, 200; UK, 150); the mean age of respondents was 64.3 years. Treatment choices revealed that patients were most concerned with treatment efficacy. However, treatment-related convenience factors, such as route of drug administration and frequency of monitoring visits, were as important as some treatment-related side effects, such as skin rash, nausea, and fatigue. Patient preferences across countries were similar, although patients in Canada appeared to be more affected by concomitant steroid use. CONCLUSION: Patients with mHSPC believe the use of ADT alone is insufficient when more effective treatments are available. Efficacy is the most significant driver of patient choices. Treatment-related convenience factors can be as important as safety concerns for patients.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".