Factors Influencing Men Undertaking Active Surveillance for the Management of Low-Risk Prostate Cancer
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To identify and describe decision-making influences on men who decide to manage their low-risk prostate cancer with active surveillance. RESEARCH APPROACH: Qualitative, semistructured interview. SETTING: The Prostate Centre at Vancouver General Hospital in Canada. PARTICIPANTS: 25 patients diagnosed with low-risk prostate cancer and on active surveillance. METHODOLOGIC APPROACH: An interpretative, descriptive, qualitative design. MAIN RESEARCH VARIABLES: Factors that influenced men's decisions to take up active surveillance. FINDINGS: The specialists' description of the prostate cancer was the most influential factor on men choosing active surveillance. Patients did not consider their prostate cancer to be life threatening and, in general, were relieved that no treatment was required. Avoiding treatment-related suffering and physical dysfunction and side effects such as impotence and incontinence was cited as the major reason to delay treatment. Few men actively sought treatment or health-promotion information following their treatment decision. Female partners played a supportive role in the decision. The need for active treatment if the cancer progressed was acknowledged. Patients were hopeful that new treatments would be available when and if they needed them. Being older and having comorbidities did not preclude the desire for future active treatment. Patients carried on with their lives as usual and did not report having any major distress related to being on active surveillance. CONCLUSIONS: The study findings indicate that men are strongly influenced by the treating specialist in taking up active surveillance and planning future active treatments. As such, most men relied on their specialists' recommendation and did not perceive the need for any adjunct therapy or support until the cancer required active treatment. INTERPRETATION: Oncology nurses should work collaborative-ly with specialists to ensure that men receive the information they need to make informed treatment decisions.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".