A survey to determine patients’ (pts) opinions of a clinical trial (CT) comparing intermittent (int) versus continuous (cont) androgen receptor signaling inhibitor (ARSi) therapy for the treatment of advanced prostate cancer.
Bibliographic record
Abstract
295 Background: Several therapies have shown a survival benefit and improvements in pt-reported quality of life in metastatic prostate cancer (mPC) including ARSis, such as abiraterone (A) and enzalutamide (E). While both are fairly well tolerated, there is a wide range of side effects associated with them. In addition, both of these medications are costly. The purpose of this study was to obtain pts’ opinions regarding potential participation in a randomized CT comparing int to cont treatment with A and E. Methods: We approached ambulatory pts diagnosed with mPC between August 2018 and October 2018 to complete a questionnaire eliciting their preferences regarding participation in a CT comparing int to cont treatment with A or E. A total of 105 patients completed the survey. We collected information regarding pts’ current/prior therapies, their interest in being treated with an int strategy and reasons for or against participating in a randomized CT with int versus cont treatment. Results: The median age of our pts was 72 years (IQR: 67 -78). 30% of patients had prior experience with an ARSi (A 10%, E 20%). 51% of pts were receiving either A (28%) or E (23%) at the time of the survey. 76% of pts indicated that they would generally be interested (27%) or very interested (49%) in participating in CTs, and 65% of patients were interested (19%) or very interested (46%) in participating in a CT involving int ARSi therapy. The reasons for participating include the prospect of less side effects (49%), less medication burden (39%), and cost-savings (15%). The reasons for not participating include the possibility of the cancer worsening off treatment (55%), the possibility of worse overall survival (48%), and the possibility of developing cancer-related symptoms in the off-phase (39%). Conclusions: The majority of pts surveyed would be interested in participating in a randomized CT that would include int versus cont treatment with an ARSi. In a complementary study, we are surveying oncologists to determine the ideal patient cohort for such a study, as well as acceptable triggers for treatment interruption and resumption.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".