Making their decisions for prostate cancer treatment: Patients’ experiences and preferences related to process
Bibliographic record
Abstract
INTRODUCTION: We sought to determine the experiences and preferences of prostate cancer patients related to the process of making their treatment decisions, and to the use of decision support. METHODS: Population surveys were conducted in four Canadian provinces in 2014-2015. Each provincial cancer registry mailed surveys to a random sample of their prostate cancer patients diagnosed in late 2012. Three registries' response rates were 46-55%; the fourth used a different recruiting strategy, producing a response rate of 13% (total n=1366). RESULTS: Overall, 90% (n=1113) of respondents reported that they were involved in their treatment decisions. Twenty-three percent (n=247) of respondents wanted more help with the decision than they received and 52% of them (n=128) reported feeling well-informed. Only 51% (n=653) of all respondents reported receiving any decision support, but an additional 34% (n=437) would want to if they were aware of its existence. A quarter (25%, n=316) of respondents found it helpful to use a decision aid, a type of decision support that provides assistance to decision processes and provides information, but 64% (n=828) reported never having heard of decision aids; 26% (n=176) of those who had never heard of decision aids wanted more help with the decision than they received compared to 13% (n=36) of those who had used a decision aid. CONCLUSIONS: The majority of respondents wanted to participate in their treatment decisions, but a portion wanted more help than they received. Half of those who wanted more help felt well-informed, thus, needed support beyond information. Decision aids have potential to provide information and support to the decision process.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".