Effect of neoadjuvant chemotherapy (NAC) on patient preferences for adjuvant treatment in muscle-invasive urothelial carcinoma (MIUC): A multi-country discrete choice experiment (DCE).
Bibliographic record
Abstract
454 Background: Patient preference is an important factor in selecting appropriate treatment choices. Although underutilized, the standard of care for MIUC is with NAC, whereas evidence for adjuvant therapy is less clear. With the introduction of novel adjuvant treatments such as immune checkpoint inhibitors, treatment options are expected to expand. This study examines whether preferences for adjuvant therapy is impacted in MIUC patients receiving NAC. Methods: A cross-sectional, web-based survey included patients ≥ 18 years old who self-reported being diagnosed with MIUC and underwent radical cystectomy or nephroureterectomy without recurrence. Patients were recruited from the US, UK, Canada, France, and Germany (May–Sep 2021). A DCE using 2 adjuvant treatment profiles included 8 attributes: cancer-free survival, overall survival (OS), hypothyroidism requiring life-long hormone therapy, risk of a serious adverse event (requiring medical intervention/possible hospitalization), nausea, fatigue, diarrhea, and a dosing regimen (frequency of treatment and monitoring); an opt-out option of no treatment was also shown. Patients were grouped according to self-reported receipt of NAC. Descriptive statistics and hierarchical Bayesian logistic model with estimated preference weights were used. Relative importance estimates (mean ± standard error), or how much the attribute ranges accounted for the variation in preferences, were computed for each attribute. Bivariate comparisons used t-tests. Results: This interim analysis identified 205 patients (70.7% of target sample; US, n = 99; Germany, n = 60; UK, n = 31; Canada, n = 14; France, n = 1). Of 82 patients (40.0%) receiving NAC, 32.7% were patients > 65 years and 55.1% were male; receipt of NAC did not differ by age ( P = 0.248) or sex ( P = 0.731). Patients were willing to accept increased risk in toxicities for increased treatment efficacy. Specifically, mean relative importance of treatment attributes showed that difference in median OS (25 months compared to 78 months) was most important (34.6% ± 1.6), although less so for those who did not receive NAC (30.2% ± 2.4 vs 37.5% ± 2.0; P = 0.022). Patients chose an adjuvant treatment option over ‘no treatment’ 91% of the time, with similar findings by NAC status. Conclusions: Preliminary data indicates that receipt of NAC impacts preferences for adjuvant treatment attributes. However, regardless of these attributes, patients still preferred adjuvant treatment over none. These results suggest that providing standard of care NAC does not reduce patient preference for adjuvant therapy; rather, patient preferences for adjuvant treatment attributes vary by treatment history, with implications for improving quality of care and outcomes.
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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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".