The Design and Implementation of a Decision-box Tool to Aid in Shared Decision-Making Regarding Prostate Biopsy
Bibliographic record
Abstract
Screening for prostate cancer is a controversial topic. Nevertheless, many men still undergo screening using the prostate-specific Antigen (PSA) marker or digital rectal examination (DRE). Once an elevated PSA level or abnormal DRE finding is detected, the patient is often referred to a specialist regarding a prostate biopsy. However, the decision whether to undergo a biopsy or not is complex, and there is often no clear choice. Thus, shared decision-making between patient and clinician becomes an extremely important element of the clinical encounter. For this project, we created a decision-box tool for patients to improve their ability to make confident and competent decisions regarding prostate biopsy, as well as to better inform patients regarding the impact and potential consequences of their decision. We have created and evaluated the decision-box tool using methods designed by previous research around such tools, which involved giving it clinicians and patients to use to assess its viability, usefulness, and clarity, and incorporating their feedback into improving the decision-box. We have integrated the tool into clinical practice at the new Prostate Diagnostic Assessment Clinic at the St Catharines Site of the Niagara Health System in Ontario, Canada. We randomized patients into 2 groups, the experimental group, which received the decision-box tool, and the control group, which did not receive the tool. We have begun to examine its effects on patients’ perceived satisfaction, confidence, and comfort level regarding their decision whether to undergo a prostate biopsy or not. Our expected outcome is that the intervention of a decision-box tool before the clinical encounter will better engage patients to make an informed decision and lead to a more confident and satisfying choice.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".