Abstract PO1-10-08: Building awareness of triple negative breast cancer: Results from the Canadian Breast Cancer Network national survey
Bibliographic record
Abstract
Abstract Background: Assessing the education, information and support needs of Canadians diagnosed with breast cancer involves categorizing and tailoring topics and methods of delivery but, most importantly, it must be rooted in an understating of the patient. In 2022, the Canadian Breast Cancer Network (CBCN) initiated a project to identify the needs of the Canadian breast cancer population, determine differences between those diagnosed with triple negative breast cancer (TNBC) compared to non-TNBC patients and develop tailored, focused programs and materials as a result of the findings. Methods: CBCN conducted a series of 45-minute key informant interviews (7) with patients and oncologists to determine needs, gaps, programs, and materials for triple negative breast cancer patients in Canada. Five 90-minute patient focus groups (32 participants) were conducted to enhance the interviews. One group was specifically for metastatic patients. Findings from the interviews and focus groups were used to inform the questions for an online survey open to all Canadians diagnosed with breast cancer which was fielded May 1 to June 10, 2022. The data analysis began in September 2022 and is on-going. Results: While 47.9% of TNBC respondents (versus 51.3% of non-TNBC patients) said that they were aware of different types and subtypes of breast cancer, 70.6% of patients reported they were not aware of the term “triple-negative breast cancer” and only learned about at diagnosis. 69.9% of TNBC patients said that the person giving them their diagnosis used the term – triple-negative breast cancer. 54.5% reported being provided with specific details about their TNBC diagnosis. This included details about: the aggressive nature of TNBC (76.8%), treatments (67.6%), treatment goals (56.3%), and the urgency of beginning treatment (66.2%). TNBC patients reported that in retrospect, more information on clinical trials (41.7%), the long-term side effects of treatment (38.6%) and post-treatment follow-up (32.5%) should have been included in the discussion at diagnosis. Conclusions: Breast cancer is not a monolithic disease. TNBC impacts 10-20% of the breast cancer population. A majority of those diagnosed with TNBC in the survey were not even aware of this subtype. Building awareness of TNBC, its risk factors and different treatment needs in the public at large and among the breast cancer community could facilitate discussions with healthcare providers and assist researchers seeking new treatments and, most importantly, provide an informed voice for those with TNBC. Making the most current, evidence-based TNBC information and resources available to both patients and providers at diagnosis could build trust and understanding of the differences in treatment and follow up and instill confidence in the overall patient experience. Citation Format: Kathleen D. Swiger, Scott Richter, Bukun Adegbembo, Cathy Ammendolea. Building awareness of triple negative breast cancer: Results from the Canadian Breast Cancer Network national survey [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-10-08.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".