Impact of pre-treatment counselling on decisional regret of prostate cancer survivors
Bibliographic record
Abstract
INTRODUCTION: Prostate cancer (PCa) impacts patient lives beyond oncologic concerns alone. PCa survivorship entails all impacts of PCa, from time of diagnosis to end of life. This may include decision regret (DR). We aimed to determine survivor experiences from a functional perspective throughout survivorship. METHODS: Our cross-sectional survey was circulated to all members of the Manitoba Prostate Cancer Support Group. Questions explored patient understanding of functional impacts concerning treatment. Survey items included binary and Likert scale questions, and an open-answered question asking how care may be improved. Responses were used to identify predictors of DR. RESULTS: A total of 514 patients received our survey, with a response rate of 23.7% (n=122). Most survivors were offered radical prostatectomy (RP) or radiation therapy, at 73.0% and 63.9%, respectively; 14.9% reported lacking understanding of treatment impact on erections. Similarly, 11.5% reported lacking understanding of treatment on urinary continence. Predictors of DR included treatment with RP and low pre-treatment understanding of potential erectile dysfunction (ED) and urinary incontinence. CONCLUSIONS: PCa survivors are at high risk of DR, particularly those who undergo treatment with RP and those who identify as having low pre-treatment understanding of potential ED and urinary incontinence. Virtual care did not impact DR. Results highlight the importance of thorough counseling on functional aspects of PCa management prior 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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".