Fatigue in Prostate Cancer: A Roundtable Discussion and Thematic Literature Review
Bibliographic record
Abstract
Context: Cancer and its treatments cause fatigue in up to 90% of men with advanced prostate cancer. As men with prostate cancer are surviving longer, cancer-related fatigue is becoming increasingly important for clinicians to understand and proactively manage. Objective: The aim of this work is to identify knowledge gaps that may support healthcare professionals to recommend personalised fatigue management strategies. Evidence acquisition: This manuscript is based on a roundtable discussion held during the European Association of Urology 2022 Annual Symposium, combined with a review of the literature. Five core themes were generated from the roundtable: (1) meaning of fatigue in prostate cancer patients, (2) impact of fatigue, (3) association between fatigue and treatment selection, (4) benefits of managing fatigue, and (5) barriers to exercise. Evidence synthesis: Cancer-related fatigue has complex underlying aetiology and is a subjective experience that may be under-reported. Some studies have shown that techniques such as education, cognitive behavioural therapy, guided imagery, and progressive muscle relaxation can result in clinically meaningful improvements in fatigue. However, the largest body of evidence, and a theme echoed in the roundtable discussions, was the benefit of exercise on fatigue. Despite the benefits of exercise, for some men, objective barriers to exercise exist and knowledge of benefits does not automatically translate into implementation and adherence. Conclusions: Understanding the specific health needs of individual patients and their desired health outcomes is essential to identify personalised strategies for minimising fatigue. As an outcome of the roundtable meeting, we developed a quick reference guide for healthcare providers. A high-resolution copy can be downloaded from https://patients.uroweb.org/library/fatigue-in-prostate-cancer-patients-guide/. Patient summary: This article is based on dialogue between a group of specialists, patients, and caregivers, which took place at a roundtable meeting during the European Association of Urology 2022 Annual Symposium. The group discussed how healthcare providers can best support their patients who experience fatigue. The group subsequently developed a guide to help healthcare providers during appointments.
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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 | 0.064 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".