Exploring the effect of medication features in renal cell carcinoma: A patient preference study.
Bibliographic record
Abstract
463 Background: Limited information exists about patient preference (PP) for renal cell carcinoma (RCC) medication profiles, especially related to uncertainty in outcomes, potential correlation between toxicity and efficacy, and dosing schedules. Methods: RCC patients in the United States and Canada completed an online survey with questions that examined PP for medication profiles by varying efficacy (progression free survival [PFS]), tolerability (fatigue, hand-foot syndrome, hypertension, diarrhea) and dosing schedules (with or without 2 week break). A discrete-choice experiment (DCE) survey tested the impact of information on correlation between PFS and toxicity on medication choice, with half of the sample randomly assigned to receive the information. Separate exploratory questions assessed patient tradeoff between higher toxicity during treatment vs. the chance of longer PFS (2+ years) and type of dosing schedules, respectively. Results: The interim survey results included 343 RCC patients (172 metastatic (mRCC), 96 currently on treatment). In the exploratory questions, 50% of mRCC patients and 51% of patients receiving treatment selected the option with higher toxicity and a 5% higher chance of longer PFS. Patients who received information on a potential correlation between tolerability and PFS were more likely to say they would tolerate worse toxicity for a 5% higher chance of longer PFS than those who did not receive the information (49% vs. 31%). A majority of RCC patients preferred a treatment with a 2-week break in the dosing schedule compared to continuous treatment (59% vs. 25%). When told side effects were worse during the treatment period for the medication with the break vs. the continuous treatment (moderate vs. mild), 42% of RCC patient, 50% of mRCC patients, and 55% patients on treatment still preferred a break. Additional DCE analysis is underway and will provide more results on patient preferences for medication profiles, including the importance of information received. Conclusions: Patients have heterogeneous preferences over medicine features and outcomes. Physicians need to provide patients with comprehensive information about medication features and efficacy to provide a personalized and optimal approach to treatment.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".