Consumer impact of an interactive decision aid for rectal cancer patients offered adjuvant therapy
Bibliographic record
Abstract
OBJECTIVE: There is increasing interest in the use of decision aids (DAs) to facilitate patient involvement in clinical decisions. This study explored the views of patients with colorectal cancer and participants in a community bowel screening service regarding an interactive DA concerning adjuvant treatment for rectal cancer, and the impact of the aid on knowledge, anxiety, attitudes and preferences for treatment options. METHOD: Fourteen patients with colorectal cancer participated in four focus groups. Eighty-nine participants in a community bowel screening service completed a questionnaire before and 1 week after viewing the DA. Thirty were randomly selected to participate in a telephone interview to obtain qualitative feedback about the DA. RESULTS: Focus group participants reported using information to evaluate their doctor's care and expertise, or to prepare themselves for future symptoms and side-effects. Most supported the use of a DA and preferred pie charts to convey risk information. Within the community sample, anxiety remained stable and knowledge increased after exposure to the DA. Almost all participants found the DA useful and easy to understand, and felt it would make the process of decision making easier. CONCLUSION: A DA regarding adjuvant therapy for rectal cancer appears to be valued and to produce positive outcomes. A randomized controlled trial of this intervention is now required.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".