How do women trade-off benefits and risks in chemotherapy treatment decisions based on gene expression profiling for early-stage breast cancer? A discrete choice experiment
Bibliographic record
Abstract
OBJECTIVES: Gene expression profiling (GEP) of tumours informs baseline risk prediction, potentially affecting adjuvant chemotherapy decisions for women with early-stage breast cancer. Since only 15% will experience a recurrence, concerns have been raised about potential harms from overtreatment and high GEP costs in publicly funded healthcare systems. We aimed to estimate preferences and personal utility of GEP testing information and benefit-risk trade-offs in chemotherapy treatment decisions. DESIGN, SETTING AND INTERVENTION: Based on literature review and findings from our qualitative research (focus groups, interviews with patients with breast cancer and medical oncologists), we developed a discrete choice experiment (DCE) survey and administered it via an internet panel. The DCE included 12 choice tasks with 5 attributes and 3 alternatives considering orthogonality, D-efficiency and level balance. PARTICIPANTS: The DCE survey was administered to 1004 Canadian women from the general population. MAIN OUTCOME MEASURES: Preferences were analysed using conditional logit and hierarchical Bayes and evaluated for goodness of fit. We conducted simulation analyses for alternative scenarios. RESULTS: GEP test score indicating likely benefit from chemotherapy was the most important attribute. Doctor's clinical estimate of the risk of cancer returning, trust in your cancer doctor and side effects of chemotherapy (temporary and permanent) were relatively less important but showed significant differences among levels. In the scenario analyses, 78% were likely to choose chemotherapy in a high-risk scenario, 55% in a moderate-risk scenario and 33% in a low-risk scenario, with the other attributes held constant. A high GEP score was more important in influencing the choice of chemotherapy for those at intermediate clinical risk. CONCLUSIONS: GEP testing information influences chemotherapy treatment decisions in early-stage breast cancer and varies depending on clinical risk. Clinicians should be aware of these differences and tailor the use of GEP testing accordingly.
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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 | 0.013 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".