Abstract PS16-09: A decision support intervention to promote the use of preventive therapy among women at high risk for invasive breast cancer
Bibliographic record
Abstract
Abstract Background: Clinical trials have reported significant breast cancer risk reduction with preventive therapy among women at high risk for invasive disease, but these medications remain underutilized. We developed risk communication and decision support tool comprised of an educational video and graphic display of benefits of preventive therapy for providers to use during patient consultations. The primary aim was to increase the uptake of preventive therapy among high-risk women. Methods: Women aged 35–69 years with a history of lobular carcinoma in situ (LCIS) or atypical hyperplasia (AH) receiving care at MD Anderson Cancer Center, Houston, Texas, were eligible to participate. After cognitive testing, a field test was performed on two patient populations before (pre-implementation) and after incorporating the tool into clinical practice (implementation). Study participants completed self-administered questionnaires including knowledge about preventive therapy, treatment preferences, decisional conflict, shared decision-making process and Ottawa acceptability scales; physicians completed surveys on their experiences with the decision support tool. Descriptive analyses and standard tests of association were performed. Results: Of the 48 female participants who completed surveys, 21 were in the implementation group. Majority of the participants were non-Hispanic (80.8%), White (75%), with a college degree or more (63.8%) and mean age of 53 years. Most participants had good knowledge about the role of preventive therapy but only 10% pre-implementation and 15% in the implementation group correctly identified that taking preventive therapy can reduce the risk of breast cancer by up to 50%. Overall, 65.2% of participants were leaning towards taking preventive therapy. Compared to those in the implementation group, women in the pre-implementation group were more likely to be unsure about their decision (34.6% vs 20.0%, p=0.088). Participants in the implementation group were less likely to take preventive therapy (57.1% vs 70.4%, p=0.428). Decision making process scores were high (3.26 vs 3.65, p=0.122) and decisional conflict was low in both groups (12.9 vs 16.1, p=0.498). While participants in the implementation group agreed that the amount of information provided by the tool was just right (80%), they found the materials slanted towards taking preventive therapy (75%). Using a psychometric assessment, physicians gave high ratings for acceptability (mean 4.1, SD 0.6), feasibility (mean 4.4; SD 0.60) and appropriateness (mean 4.2, SD 0.6) of the tool and were satisfied/very satisfied (83.3%) with the tool. Conclusion: Although study participants had good health literacy, the majority were unaware of the significant benefit of preventive therapy in reducing breast cancer risk. A greater percentage of women in the pre-implementation group were unsure about their decision compared to women who received the tool but after receiving the tool, women in the implementation group were less likely to agree to preventive therapy. These findings suggest that the decision support tool might reduce the proportion of patients uncertain about preventive therapy but increase preference for not starting treatment. The next steps are to enhance provider discussions on the benefits of preventive therapy, test the decision support tool in less educated and underrepresented minority populations, and track adherence to preventive therapy in patients who receive the tool. Citation Format: Inimfon Jackson, Lisa Lowenstein, Parijatham S. Thomas, Therese Bevers, Viola Leal, Jurnie Hinde, Robert J. Volk, Abenaa M. Brewster. A decision support intervention to promote the use of preventive therapy among women at high risk for invasive breast cancer [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr PS16-09.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".