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Record W4405221704 · doi:10.1093/jsxmed/qdae167.096

(098) IMPACT OF PRE-TREATMENT COUNSELLING ON DECISIONAL REGRET OF PROSTATE CANCER SURVIVORS: CROSS-SECTIONAL ANALYSIS OF PATIENT REPORTED EXPERIENCE FOLLOWING DIAGNOSIS OR TREATMENT

2024· article· en· W4405221704 on OpenAlexaboutno aff
T Southall, David Chung, Karim Sidhom, Jasmir G. Nayak, Priyanka Patel

Bibliographic record

VenueThe Journal of Sexual Medicine · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsnot available
Fundersnot available
KeywordsRegretMedicineProstate cancerCross-sectional studyCancerGynecologyClinical psychologyOncologyInternal medicinePathology

Abstract

fetched live from OpenAlex

Abstract Introduction Prostate cancer (PCa) impacts patient lives beyond oncological concerns alone. PCa survivorship includes the mental, physical, sexual, emotional, and financial effects patients endure from time of diagnosis, through treatment, and beyond. For some, this includes decision regret (DR). We aim to determine survivor experiences and understanding from a functional perspective throughout survivorship. Objective Our objective was to describe the local experiences and perspectives of PCa survivors regarding their functional outcomes at both the pre and post treatment level. Additionally, we sought to investigate the relationship between patients’ understanding of sexual and urinary side effects of treatment as well as modality of care delivery (ie virtual or in person) and development of treatment DR. Methods This is a cross-sectional survey of survivors. Our survey was circulated to all members of the Manitoba Prostate Cancer Support Group. Topics included erectile dysfunction (ED), penile shortening, urinary incontinence, and patient understanding throughout their care. Survey items included binary and Likert scale questions regarding patient understanding and treatment impact, as well as an open answered question asking how survivorship care may be improved. Responses were used identify predictors of DR. Results 514 patients received our survey with a response rate of 23.7% (n = 122). The average age of diagnosis and treatment were 65.2 and 65.9 respectively. Most common treatments included 63.9% radical prostatectomy (RP), 54.1% radiotherapy (RT), and 36.1% androgen deprivation therapy (ADT). 71.9% reported sexual health to be very or somewhat important, but 27.9% reported no pre-treatment discussion of potential ED. 14.9% reported lacking understanding of treatment impact on erections. 76.9% reported no counselling on penile shortening and 95.0% reported no counselling on climacturia, prior to treatment. 27.3% reported no pre-treatment discussion of urinary incontinence and 11.5% reported lacking understanding of treatment impact on urination. Predictors of DR included treatment with RP, and low pre-treatment understanding of potential ED and urinary incontinence. Common open answer responses included desire for more information regarding support groups, treatment side effects, and management of such. Conclusions PCa survivors are at high risk of DR. Virtual care does not seem to impact DR. However, the degree of pre-treatment understanding of treatment options and their functional impact does. Survivors are motivated to learn about their condition, requesting more information on treatments, side effects, and local support groups. Apart from PCa diagnosis and management, it is equally as important to discuss functional aspects. Disclosure No.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.076
GPT teacher head0.372
Teacher spread0.296 · 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 designObservational
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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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