Abstract P2-09-03: Got patient advocates? The value of patient advocate participation in a large research study to develop personalized risk-based breast cancer screening strategies
Bibliographic record
Abstract
Abstract Background: Over the last several years there has been confusion among women about breast cancer screening and patient advocates are increasingly used to help women understand the changes. In 2009, the U.S. Preventive Task Force (USPSTF) recommended that women under the age of 50 do not need routine screening. New state laws require breast density results to be given to women and their providers for making their screening choices. Women are not sure what to do with this information and are offered little guidance to personalize their screening recommendations. The goal of this study is to develop a risk model for improved personalized breast cancer screening recommendations. Patient advocates were incorporated throughout the design, recruitment, analysis, and dissemination phases of the study. Methods: The research team included three patient advocates who participated as full members in the bi-weekly and quarterly team meetings throughout the duration of the study. All advocates were breast cancer survivors. The primary components of the study included focus groups to understand women’s knowledge and views on breast density as well as personalized screening, a telephone survey to gain a broader view on these topics, and recruitment to a case:control study to build a breast cancer risk model that incorporates an automated measure of breast density. Results: Enrollment was completed over one year with 3,445 women; 839 cases and 2,606 controls. Study design and resulting recruitment strategies were reviewed early with regular feedback by the patient advocates. At the advice of the advocates, Facebook was chosen as primary social media, resulting in nearly 200 posts (stories) and 1583 likes for the project. Many of the posts were generated by or featured advocates. Regarding the focus groups, the advocates developed the questions. Women were informed about the study by the advocates and educated about breast density. The advocates were key in using the focus groups to find the right language for enrollment materials, obtain their perception of the importance of the study, and understand their views regarding a new model for personalized screening for women. The advocates were likewise key in developing questions for and analyzing results of the telephone survey. In the analysis phase, the advocates assisted the team in understanding the results of the risk questionnaires. For example, most women did not know the type of breast cancer that they had been diagnosed with or even if it was invasive. The advocates confirmed how and why even highly educated women would not necessarily retain this information. Finally, the advocates will have a strong role in the eventual dissemination of the study findings to women. Conclusions: The investigators have developed a breast cancer risk model that includes an automated measurement of breast density, with the goal of personalizing screening for women. The inclusion of patient advocates throughout all phases of the study improved knowledge and insight of the investigating team. Their role extended beyond community engagement and development of study materials. The advocates became integral members of the study team. Citation Format: Vernal Branch, Carolyn Achenbach, Kathleen G Ross, Wendy F Cohn, Martin D Yaffe, William A Knaus, Jennifer A Harvey. Got patient advocates? The value of patient advocate participation in a large research study to develop personalized risk-based breast cancer screening strategies [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P2-09-03.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.236 | 0.280 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".