Abstract 1293: Uncovered: The breast cancer journey of a Black woman from advocacy to awareness to action
Bibliographic record
Abstract
Abstract Background: There have been 3 significant life events guiding my journey from being a young, Black mother with stage 4 breast cancer and thyroid cancer to an engaged patient partner. The first was having a benign breast tumor removed at age 14. The second was my 2017 mBC diagnosis, including my bilateral mastectomy and the lack of images and information available for Black women concerning reconstruction and scar management. The third is exclusion from and the lack of diversity in clinical trials. Objective: In order to address the disparities in cancer and my unmet needs, I use my story and experiences as a catalyst for change primarily in three forms, which have evolved over time: advocacy, awareness, and action. Methods: Advocacy: To unburden myself from the shame about my breast health and the scar from my surgery at age 14, I vocalized my concerns and needs as an adult navigating my breast reconstruction options after my 2017 cancer diagnosis. Ongoing frustration that my needs were not being addressed led me to create Uncovered: A Breast Recognition Project, in collaboration with Rethink Breast Cancer. Uncovered, a digital and print resource, shines a light on the disparities and challenges that Black, Indigenous and people of color face in breast cancer care. Awareness: The 2020 edition started with my story and those of 7 Black women. After Uncovered launched, other racialized women reached out with similar experiences. The resource was expanded in 2021 to include stories and images of an Inuk woman, Black women, and women of color. This confirmed a systemic need to raise awareness and address racialized communities and inequities in breast cancer care. Action: As a result of the slow progress towards systemic change, I, along with the women of Uncovered, co-created the Wish List in 2021. The Uncovered Wish List is 6 accessible and actionable items that should be a priority for ending disparities in breast cancer care. Due to the complexity of my diagnosis, which limits my clinical trial options, and the historical underrepresentation of racialized communities in clinical trials, I now partner with cancer institutions and organizations that focus on research to address ongoing unmet needs with the goal to improve equity, diversity and inclusion. Results: Uncovered is a first-of-its-kind resource in Canada. To date, it has had a global reach of over 70 million through conferences, panels, podcasts, resource links, presentations, and various media. The recognition of my story, Uncovered, and the Wish List allows me to form collaborative connections, and become an engaged patient partner committed to systemic change. Conclusion: Despite systemic barriers, it is possible for Black women with breast cancer to advocate, raise awareness and take action towards change in meaningful ways. Citation Format: Michelle Audoin. Uncovered: The breast cancer journey of a Black woman from advocacy to awareness to action [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1293.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".