Patient Preferences for HR+/HER2− Early Breast Cancer Adjuvant Treatment: A Multicountry Discrete Choice Experiment
Bibliographic record
Abstract
Introduction: More adjuvant treatment options are becoming available for hormone receptor-positive/human epidermal growth factor receptor 2-negative (HR+/HER2-) early breast cancer (EBC) based on results of clinical trials. This study quantified the importance of different attributes of EBC adjuvant therapies to patients and the benefit-risk tradeoffs patients were willing to make. Methods: = 40). Participants (pts) made 10 choices between pairs of hypothetical treatments described by varying levels of 6 attributes. DCE data were analyzed using a correlated mixed logit model. Relative attribute importance scores captured the impact of each attribute across clinically relevant ranges. Benefit-risk tradeoffs were captured as the minimum improvements in 5-year invasive disease-free survival (iDFS) that pts would require to tolerate increases in therapy-associated adverse event (AE) risks. Results: A total of 866 patients from the USA, France, Spain, Canada, the UK, Germany, South Korea, and Australia completed the DCE (mean age: 57.7 years; 76% postmenopausal; 29% stage I disease, 55% stage II, 16% stage III). Improved 5-year iDFS (75.4-82.7% range; associated with combination regimens [CRs] vs. endocrine therapy [ET] alone) contributed the most to treatment preferences (clinically relevant relative attribute importance: 38.4%), followed by reduced risks of venous thromboembolic events (VTEs) (20.4%), neutropenia (20.3%), and diarrhea (15.0%). Treatment type + duration (3.7%) and fatigue (2.3%) were less important. Pts required the largest improvement in 5-year iDFS (3.9%) to tolerate increased risks of VTE (0.7%-2.5%) or neutropenia (5.6%-46%); willingness to accept tradeoffs depended on the AE. Preference heterogeneity was observed across subgroups, but 5-year iDFS improvement was consistently the most impactful on treatment choice in all subgroups. Conclusion: A multicountry sample of patients most valued adjuvant therapies with higher 5-year iDFS and may therefore prefer CRs over ET alone. The value of CRs depends on their specific safety profiles, and shared decision-making should consider this to select treatment options that align with individual preferences.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".