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PD16-07 POST-PROSTATECTOMY ADJUVANT ANDROGEN DEPRIVATION THERAPY-- PATIENT OPINIONS AND GOALS OF CARE

2023· article· en· W4360605497 on OpenAlexaboutno aff
Max Levitt, Ameeta L. Nayak, Dean Fergusson, Luke T. Lavallée, Christopher Morash, Ilias Cagiannos, Rodney H. Breau

Bibliographic record

VenueThe Journal of Urology · 2023
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAndrogen deprivation therapyProstatectomyProstate cancerAdjuvant therapyAdjuvantClinical trialCancerOncologyGynecologyUrologyGeneral surgeryInternal medicineFamily medicine

Abstract

fetched live from OpenAlex

You have accessJournal of UrologyCME1 Apr 2023PD16-07 POST-PROSTATECTOMY ADJUVANT ANDROGEN DEPRIVATION THERAPY-- PATIENT OPINIONS AND GOALS OF CARE Max Levitt, Ameeta Nayak, Dean Fergusson, Luke Lavallee, Christopher Morash, Ilias Cagiannos, and Rodney Breau Max LevittMax Levitt More articles by this author , Ameeta NayakAmeeta Nayak More articles by this author , Dean FergussonDean Fergusson More articles by this author , Luke LavalleeLuke Lavallee More articles by this author , Christopher MorashChristopher Morash More articles by this author , Ilias CagiannosIlias Cagiannos More articles by this author , and Rodney BreauRodney Breau More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003271.07AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Despite the proven benefit of adjuvant androgen deprivation therapy (ADT) for patients receiving primary radiation, few studies evaluate adjuvant ADT after prostatectomy. We surveyed Canadian prostate cancer patients about adjuvant ADT with the goal of informing an adjuvant ADT clinical trial. METHODS: An electronic survey was devised and distributed using a modified Dillman approach. The survey was sent to members of Prostate Cancer Canada, a patient advocacy group. In addition to demographic information, we asked patients about their experience with prostate cancer, if they received post-operative therapy, and about their opinions to inform the design of an adjuvant ADT clinical trial. The survey was sent on May 2021, and all responses were received by July 2021. RESULTS: Forty patients completed the survey. The average participant age was 71±7.3 years old. The average age at prostate cancer diagnosis was 64±6.7 years. Thirty-eight (95%) patients were previously treated with radical prostatectomy and 24 (60%) subsequently developed biochemical recurrence. If it had been available, 30 (75%) participants indicated that they would have been interested in an adjuvant ADT trial to prevent biochemical recurrence. Most (15; 37.5%) stated that 12 months would be the longest duration of ADT that they would consider. The remainder of participants would have considered up to 6 months (9; 22.5%), up to 18 months (3; 7.5%), up to 24 months (5; 12.5%), or greater than 24 months (8; 20%) of adjuvant ADT. A daily oral tablet (31; 52.5%) or injection every 6 months (9; 22.5%) were favoured in a clinical trial over an injection at shorter time intervals. The most important outcomes for a trial of adjuvant ADT were prevention of cancer related death (38; 95%) and cancer recurrence (37; 92%). If 1 year of adjuvant ADT reduced PSA recurrence by 50%, many (29; 47.5%) stated they would have chosen this intervention. CONCLUSIONS: Few trials have assessed adjuvant ADT after radical prostatectomy and many patients claim they would have been interested in participating in a trial if it had been available. Based on these results, a randomized trial is warranted and patient preferences should be incorporated in trial design. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e491 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Max Levitt More articles by this author Ameeta Nayak More articles by this author Dean Fergusson More articles by this author Luke Lavallee More articles by this author Christopher Morash More articles by this author Ilias Cagiannos More articles by this author Rodney Breau More articles by this author Expand All 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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.070
Threshold uncertainty score0.233

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0700.015

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.

Opus teacher head0.025
GPT teacher head0.317
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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