Using a web-based decision support intervention to facilitate patient-physician communication at prostate cancer treatment discussions
Bibliographic record
Abstract
Purpose: To measure the preferences and values of men newly diagnosed with prostate cancer (PC) using a web-based decision support technology—the Decision Support Intervention-Prostate Cancer (DSI-PC). Methods: Health information seeking behaviour, factors having an influence on the treatment decision, decision control, and preferred treatment were recorded by the DSI-PC program prior to the treatment consultation. A summary page of responses was provided to each patient to use at treatment discussions. Measures of decision control and decision conflict were measured prior to the treatment discussion, and following a treatment decision. Patient satisfaction was measured after a treatment decision had been made. Results: Forty-nine men completed the DSI-PC program prior to their treatment discussion. Sixty-one per cent shared the summary sheet with their physician/s when discussing treatment options. The majority (63%) of patients wanted access to in-depth or detailed information. Impact of treatment on survival, urinary function, bowel function, and physician’s treatment recommendation were the four factors having the most influence on patients’ treatment decisions. Patients reported high levels of satisfaction with their treatment decision, and involvement in treatment decision making (TDM). Levels of decision conflict were significantly lower (p < 0.001) after a treatment decision was made, and men reported assuming a significantly more active role in TDM than originally preferred (p = 0.038). Conclusions: Results suggest that the DSI-PC intervention may be a useful tool to help patients identify and communicate their values and preferences to physicians at the time of treatment discussions. Key words: prostate cancer, patient-physician communication, decision support intervention
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".