Preference for breast cancer risk reduction hormonal therapy in women age 50-69 years attending screening mammography.
Bibliographic record
Abstract
Abstract Abstract #3100 Background: It is expected that tamoxifen, raloxifene and aromatase inhibitors (AIs) will become available for the indication of breast cancer prevention in postmenopausal women. It is unknown how women weigh the benefits and disadvantages of these drugs. The aim of this study was to elicit preferences, using a discrete choice experiment (DCE), for attributes of breast cancer risk reduction hormonal therapies amongst women age 50 – 69 years who attend screening mammography.
 Methods: 500 potentially eligible women were randomly sampled from a screening mammography database (SMD). Women permitting contact from the investigators were invited to participate. Demographic and breast cancer risk factor data was collected from the SMD for participants and non-participants, and compared. The DCE was administered via a postal survey. The attributes and levels for the DCE are outlined in Table 1. A fractional factorial design and matched fold-over technique yielded 16 choice sets. Two dominant choice sets were added. For each choice set, respondents were asked to mark preference for Drug A, Drug B or Neither. The DCE was analyzed using conditional logistic regression (CLR). Regression post estimation calculations (RPECs) were used to predict chemoprevention uptake.
 
 Results: 111 women agreed to be contacted by the investigators, 94 agreed to be sent a survey and 79 returned a completed questionnaire. The proportions of participants who were nulliparous or had a predicted 5-year breast cancer risk >1.66% were significantly higher than for non-participants (p<0.05). No differences were found for age, education or mammographic breast density. From the CLR, all attributes, except decreased risk bone fracture, had coefficients that were significantly different from zero (p<0.01) and of the expected sign. RPECs suggest that the uptake of tamoxifen, raloxifene and AIs would be 13%, 50% and 23% respectively, and that 14% would not consider chemoprevention.
 
 Conclusions: The most important attribute in women's selection of a prevention drug was breast cancer risk reduction. However, the following attributes were still important negative influences on choice: increased risk bone fracture > endometrial cancer > venous thromboembolic event >> cost. Raloxifene may be favoured if all three agents were available. Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 3100.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".