MP73-02 PATIENT UNDERSTANDING REGARDING END-OF-LIFE PROSTATE CANCER AND PERSPECTIVES REGARDING COST/BENEFIT OF CURRENT TREATMENT PARADIGMS
Bibliographic record
Abstract
You have accessJournal of UrologyProstate Cancer: Advanced I1 Apr 2015MP73-02 PATIENT UNDERSTANDING REGARDING END-OF-LIFE PROSTATE CANCER AND PERSPECTIVES REGARDING COST/BENEFIT OF CURRENT TREATMENT PARADIGMS Tarik Benidir, Antonio Finelli, Rob Hamilton, Karen Hersey, Anthony Joshua, Girish Kulkarni, Alexandre Zlotta, and Neil Fleshner Tarik BenidirTarik Benidir More articles by this author , Antonio FinelliAntonio Finelli More articles by this author , Rob HamiltonRob Hamilton More articles by this author , Karen HerseyKaren Hersey More articles by this author , Anthony JoshuaAnthony Joshua More articles by this author , Girish KulkarniGirish Kulkarni More articles by this author , Alexandre ZlottaAlexandre Zlotta More articles by this author , and Neil FleshnerNeil Fleshner More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2015.02.2678AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Many new drugs are now available which extend life for patients with castration resistant prostate cancer (CRPC). Excluding Docetaxel, annual costs vary from 40,000$-93,000$ for mean survival extensions of 2.1-4.8 months. This study aims to elucidate patients general understanding of CRPC, gain patient-derived opinions on costs/benefit of currently marketed CRPC drugs and assess if/when patients would forego CRPC drug therapy in a medical system where most drugs are provided free of charge. We also sought out to see if patients would agree to deny treatment in lieu of a one-time end of life premium of $50,000. METHODS We conducted a survey on prostate cancer (PCA) patients with various clinical disease states. Our survey collected patient demographics, PCA status, opinions on CRPC drug efficacy (based on hypothetical scenarios) and perspective changes if CRPC drug costs moved from funded to “out-of-pocket” expenditures. A novel treatment was proposed: opt out of life-prolonging drug treatment for a financial pay-out of $50,000. Chi-squared, Fisher's exact, and T –testing was used when appropriate. RESULTS One hundred and three patients completed the survey. CRPC was not understood by 79% of respondents. Most felt Abiraterone was helpful and should be offered to all patients (71%). A majority (65%) felt Enzalutamide should be offered for Abiraterone failures despite evidence of efficacy. A minority felt Alpharedin was helpful to all (30%). Despite any evidence, a majority (58.2%) felt combination drugs should be given to all, even if costs exceeded $250,000. Out-of-pocket scenario's shifted opinions. If patients had to pay, only 29% were willing to exceed $40,000. When asked if they would forego therapy for $50,000, a majority agreed/considered (60%). Marital status, children and PCA status did not correlate to opinions on drugs/financial stipends whereas education and income status did (p=0.03 and 0.04 respectively) with better educated/higher income patients being more likely to undervalue the benefit of CRPC drugs and take the end-of life premium. CONCLUSIONS A large gap exists regarding patient understanding of CRPC prognosis and drug value. Canadian patients overvalue drugs when paid by third parties but see less value if they have to pay “out-of-pocket”. Contrary to our hypothesis, patients with high education/income are more likely to undervalue the benefit of new drugs and more likely to accept an end-of-life premium. More realistic discussions between CRPC patients and their doctors about benefits of CRPC drugs is needed. © 2015 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 193Issue 4SApril 2015Page: e929 Advertisement Copyright & Permissions© 2015 by American Urological Association Education and Research, Inc.MetricsAuthor Information Tarik Benidir More articles by this author Antonio Finelli More articles by this author Rob Hamilton More articles by this author Karen Hersey More articles by this author Anthony Joshua More articles by this author Girish Kulkarni More articles by this author Alexandre Zlotta More articles by this author Neil Fleshner More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.193 | 0.031 |
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".