Preferences of Canadian Patients and Physicians for Treatment of HR+/HER2− Advanced Breast Cancer
Bibliographic record
Abstract
(1) Background: Past research suggests that patients with advanced breast cancer prefer treatments with improved clinical outcomes and lower risk of side effects. Evidence on preferences of Canadian patients and physicians for treatments for advanced breast cancer is limited. (2) Methods: Patients' and physicians' preferences for treatments for HR+/HER2-, pre-/peri-menopausal advanced breast cancer were assessed by an online discrete choice experiment (DCE). Treatment alternatives were characterized by seven attributes regarding dosing, efficacy, and toxicities, with levels corresponding to those for ribociclib plus a non-steroidal aromatase inhibitor (NSAI), NSAI, and tamoxifen. For patients, impacts of advanced breast cancer on quality of life (QOL) and ability to work/perform activities of daily living also were assessed. Patients were recruited by a Canadian breast cancer patient advocacy group through email and social media. Physicians were recruited by email. (3) Results: Among 118 patients starting the survey, 23 completed ≥ 1 DCE question (19%). Among 271 physicians who were sent the e-mail invitation, 21 completed ≥ 1 DCE question (8%). For both patients and physicians, the increased probability of remaining alive and without cancer progression over 2 years was the most important attribute. A treatment with attributes consistent with ribociclib plus NSAI was chosen by patients and physicians in 70% and 88% of the time, respectively. A substantial proportion of patients reported worrying about future diagnostic tests and their cancer getting worse; (4) Conclusions: Canadian patients and physicians are generally concordant in preference for advanced breast cancer treatments, preferring ribociclib plus NSAI to other options.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".