Effect of a Decision Aid on Knowledge and Treatment Decision Making for Breast Cancer Surgery
Bibliographic record
Abstract
CONTEXT: The long-term results of randomized trials have demonstrated equivalent survival rates for mastectomy and breast-conserving therapy for the treatment of early stage breast cancer. Consequently, the choice of treatment should be based on a patient's preferences. OBJECTIVE: To evaluate the impact of a decision aid regarding the different surgical treatment options on patient decision making. DESIGN AND SETTING: A cluster randomized trial for which general surgeons in the communities of central-west, and eastern Ontario, Canada, were randomly assigned to use the decision aid or not in the surgical consultation. Patients received the decision aid or not based on the surgeon seen. PARTICIPANTS: Twenty surgeons participated in the study. Of the 208 eligible women with newly diagnosed clinical stage I or II breast cancer seen by study surgeons, 201 agreed to be evaluated: 94 were assigned to the decision board and 107 to usual practice. Patients were recruited from November 1999 to April 2002. INTERVENTION: The decision board is a decision aid designed to help physicians inform their patients about different treatment options and to enable patients to express a preference for treatment. MAIN OUTCOME MEASURES: Patient knowledge about the surgical treatment of breast cancer; decisional conflict; satisfaction with decision making; and the treatment decision following the consultation. RESULTS: Patients in the decision board group had higher knowledge scores about their treatment options (66.9 vs 58.7; P<.001), had less decisional conflict (1.40 vs 1.62, P =.02), and were more satisfied with decision making (4.50 vs 4.32, P =.05) following the consultation. Patients who used the decision board were more likely to choose BCT (94% vs 76%, P =.03). CONCLUSIONS: The decision board was helpful in improving communication and enabling women to make a choice regarding treatment. Such instruments should be considered by surgeons when communicating the different surgical options to women with breast cancer.
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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 | 0.006 | 0.044 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".