Prostate cancer care in Canada: Informed decision-making, patterns of care, and treatment trends.
Bibliographic record
Abstract
289 Background: Because treatment options for localized prostate cancer (PCa) have similar survival outcomes but varying side effects, it is important that patients are meaningfully involved in the decision-making process to ensure the chosen treatment aligns with their needs, wants and preferences. Here, we describe PCa patients’ experience with informed decision-making as well as treatment patterns and trends over time. Methods: Focus groups were conducted with 47 men treated for PCa across Canada to understand their cancer journey experience. Thematic analysis was conducted. A subset of this data on informed decision-making is described. Men (≥ 35 years) diagnosed with localized, low-risk PCa from 2011-2013 were identified using data from six provincial cancer registries. Treatment data were identified by linking hospital/cancer centre data with registry data. Descriptive statistics were generated to describe treatment patterns and trends. Results: Focus group participants expressed a desire to be involved in the treatment decision-making process. While many participants felt completely informed about the treatment choices available to them, others felt they had not been properly engaged in the treatment decision-making process. Some participants felt they had opted for surgery or radiation therapy (RT) without full knowledge of the trade-offs between potential benefits and side effects. Others felt they may have made different decisions about their care had they been more informed. From registry data, in 2013 surgery was the most common primary treatment for men with low-risk PCa ranging from 12.0% in New Brunswick to 41.7% in Nova Scotia. RT was the second most common ranging from 6.4% in New Brunswick to 18.3% in Saskatchewan. Varying majorities of men had no record of surgical or radiation treatment, a proxy for active surveillance. Treatment trends over time suggest an increase in the use of non-active treatment approaches from 60.7% in 2011 to 69.9% in 2013. Conclusions: System performance indicators yield useful information about oncology practice patterns and trends. This information is enhanced when combined with patient level information on how men felt about decision-making around their PCa care.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".