Perspectives of health care professionals on active surveillance for the management of prostate cancer.
Bibliographic record
Abstract
81 Background: Active surveillance has gained widespread acceptance as a safe approach for patients with low risk prostate cancer. Despite presenting several advantages for both patients and the health care system, active surveillance is not adopted by all eligible patients. In this study, we evaluated the factors that influence physicians to recommend active surveillance and the barriers that impact adherence to this approach. Methods: We conducted five focus groups with a total of 48 health care providers (HCP) including family physicians, urologists, surgeons, radiation oncologists, fellows, and residents/medical students. These participants were all providing care for men with low risk prostate cancer and had engaged in conversations with men and their families about active surveillance. The experience of these HCP from academic hospitals in four Canadian provinces was captured. A content and theme analysis was performed on the verbatim transcripts to understand HCP decisions in proposing active surveillance and reveal the facilitators that affect the adherence to this approach. Results: Participants agreed that active surveillance is a suitable approach for low risk prostate cancer patients, but expressed concerns on the rapidly evolving and non-standardized guidelines for patient follow-up. They raised the need for additional tools to appropriately identify the patients best suited for active surveillance. Collaborations between urologists, radiation-oncologists, and medical oncologists were favoured, however, the role of general practitioners remained controversial once patients were referred to a specialist. Conclusions: Integration of more reliable tools and/or markers, and more specific guidelines for patient follow-up would help both patients and physicians in the decision-making for active surveillance.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".