Development and evaluation of a decision aid for patients considering first‐line chemotherapy for metastatic breast cancer
Bibliographic record
Abstract
OBJECTIVE: Treatment decisions in advanced breast cancer are complex, with enhanced quality of life and survival among important treatment goals. Patients with metastatic breast cancer face the decision of whether or not to have chemotherapy, and many wish to be involved in this decision. We report the development and evaluation of a decision aid (DA) designed to assist patients facing this treatment decision. DESIGN AND SAMPLE: Women with metastatic breast cancer (n = 17)and medical oncologists in Australia and Canada (n = 7) were invited to evaluate the DA. INTERVENTION: A DA was developed for patients with hormone resistant metastatic breast cancer considering chemotherapy. The DA presented options of supportive care, with or without chemotherapy. Potential benefits and side effects of different chemotherapy regimens, and evidence-based prognostic estimates were described,and a values clarification exercise included. MAIN OUTCOME MEASURES: Patient questionnaires evaluating information and decision involvement preferences, attitudes toward the DA and oncologist feedback regarding attitudes toward the DA. RESULTS: Seventeen patients participated; fifteen desired as much information about their illness as possible; sixteen wished to be actively involved in the decision-making process. The majority rated the DA as highly acceptable, clear and informative, and would recommend it to others facing this treatment decision. CONCLUSION: This is the first DA for patients with advanced metastatic breast cancer considering chemotherapy. A randomized trial is underway to evaluate its role in clinical decision-making.
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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 | 0.010 | 0.036 |
| 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 | 0.001 | 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".