Use of Conjoint Analysis to Assess Breast Cancer Patient Preferences for Chemotherapy Side Effects
Bibliographic record
Abstract
OBJECTIVE: Our objective was to evaluate preferences associated with grade I/II and grade III/IV chemotherapy side effects among breast cancer patients receiving chemotherapy. We also assessed trade-offs that patients are willing to make between treatment side effects and the route and schedule of treatment administration. METHODS: In this cross-sectional study, patients receiving chemotherapy for breast cancer completed a one-time Web survey. Conjoint analysis was used to elicit preferences for 17 grade I/II and III/IV side effects associated with available chemotherapies and regimens. In the analysis, the risk of each side effect was increased by 5%, holding all others constant, and the respective impact on patient preferences was identified. RESULTS: A total of 102 women participated (mean age 54 ± 11). Among the grade I/II side effects, a 5% reduction in the risk of sensory neuropathy, nausea, and motor neuropathy had the highest impact on preferences. Among grade III/IV side effects, motor neuropathy, nausea/vomiting, and myalgia made the most difference. An oral twice-daily regimen was most preferred; however, patients were willing to receive an intravenous regimen relative to oral to avoid an increased risk of 5% in the majority of side effects. Avoiding an increased chance of grade III/IV motor neuropathy was associated with willingness to tolerate one of the least preferred administration schedules. CONCLUSION: This study identified relative preferences among both mild/moderate to severe side effects from the patient perspective. Patients appear to be willing to make trade-offs between side effects and different regimens. These findings may help to inform medical decision-making processes.
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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 | 0.019 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".