Patient preferences for treatment and outcomes in hormone-sensitive prostate cancer (HSPC).
Bibliographic record
Abstract
e18757 Background: Treatment options for patients with HSPC have broadened, and data regarding patient preferences for therapies can aid in therapeutic decision-making. This study evaluated the impact of attributes associated with therapies for US patients with locally advanced prostate cancer (LAPC) or metastatic HSPC (mHSPC) from the perspective of patient preferences. Methods: An online discrete choice experiment (DCE) was developed for patients with LAPC or mHSPC. The DCE included 12 questions designed to systematically require tradeoffs between treatment attributes of efficacy (5-year overall survival [OS]), tolerability (fatigue, skin rash, neurotoxicity, and common chemotherapy-related toxicity), and convenience (administration factors [route, frequency, and setting], concomitant use of steroids, and monitoring requirements). Respondents could choose androgen deprivation therapy (ADT) alone or with hypothetical therapies that improved 5-year OS but had additional adverse events (AEs). Attribute-specific importance weights measuring their relative impact on treatment choices were estimated using a mixed-logit model, which also controlled for heterogeneity in preferences. Results: From September 3 to October 14, 2021, 82 respondents (mean age 61 years) completed the survey (LAPC, n = 40; mHSPC, n = 42), with 61 (74.4%) receiving ADT at the time of the survey. Respondents reported treatment efficacy (36% [95% confidence interval (CI) 22, 49]) as the most important aspect of treatment choice, followed by changes in chemotherapy-related toxicity (13% [95% CI 3, 22]) and the need for concomitant steroid use (12% [95% CI 5, 19]). Respondents considered monitoring requirements (8% [95% CI 5, 19]) to be more important than fatigue (5% [95% CI 2, 13]). Administration factors were comparable in importance to therapy AEs (Table). Respondents preferred, by at least 10 percentage points, adding therapies to ADT that could improve 5-year OS, at the detriment of additional AEs. Conclusions: After efficacy, convenience was considered to impact treatment choices at a rate comparable to tolerability issues, potentially influenced by perceived COVID-19 exposure risks. Patients with LAPC and mHSPC prioritize efficacy despite the detriment of additional AEs.[Table: see text]
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".