When the goal is not cure: A randomized trial of a patient decision aid in advanced colorectal cancer
Bibliographic record
Abstract
6509 Background: With improvements in treatment and supportive care, decisions in advanced cancer are increasingly complex. To facilitate decision-making, we developed a decision aid (DA) for patients considering first-line chemotherapy for incurable colorectal cancer, reviewing treatment options, prognostic information and toxicities. We examined its impact on patient understanding, consultation and decision- making satisfaction, anxiety, decisional conflict, quality of life, information preferences, and treatment decisions made. Methods: 207 newly diagnosed advanced colorectal cancer patients at 5 cancer centers in Australia and Canada were randomized to receive either a standard medical oncology consultation, or the same plus the DA, (take-home booklet with audio-recording, selected review by oncologist). Results: 100 were randomized to the control arm, 107 to receive the DA. Sample characteristics: median age 62 years, 58% male, 89% PS 0/1, 36% prior adjuvant chemotherapy. Patients in the DA arm demonstrated a greater increase in understanding of prognosis, options, benefits and toxicities (+19% vs +5.6%, p 0.001), with higher overall understanding (72% vs 60%, p<0.0001). Decision and consultation satisfaction, decisional conflict and quality of life were similar between groups. Anxiety, measured serially over 4–6 weeks, was similar and decreased over time. Most arrived at a decision during the first consultation; 80% chose chemotherapy, 7.5% supportive care alone, 10.5% a wait and watch strategy, with no differences between arms. 87% wanted as much information as possible, 82% wished to share decision-making with the physician, and only 15% felt the doctor alone should make the decision. More patients who received the decision aid felt they received all possible details about therapy, (72% vs 63%). 90% felt the decision was shared in part between physician and patient. Conclusion: This first randomized trial of a decision aid in advanced cancer patients shows that its use in advanced colorectal cancer improved patient understanding of prognosis, treatment options, risks and benefits without increasing anxiety. Decision aids can improve informed consent and decision-making, and can be tested through randomized trials even in the setting of advanced cancer. No significant financial relationships to disclose.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".