Abstract P4-06-09: Does Age Impact Patient Involvement in Treatment Decision Making: Results from the Canadian Breast Cancer Network (CBCN) Assessment Project
Bibliographic record
Abstract
Abstract Assessing the information and support needs of Canadians diagnosed with breast cancer must be rooted in an understating of the patient. In 2022, the Canadian Breast Cancer Network (CBCN) initiated a project to identify the educational needs of Canadian breast cancer patients in order to develop tailored, focused programs and materials to fill educational gaps. We conducted 45-minute key informant interviews (7) with patients and oncologists to determine the needs and educational gaps of breast cancer patients. Five 90-minute patient focus groups (32 participants) were conducted to enhance the interviews. Findings from the interviews and focus groups were used to inform the questions for an online survey fielded between May 1 and June 10, 2022. Data analysis began in September 2022 and is on-going.A review of survey findings of patients under 50 years (U50) (n=131) and those 70 years plus (70+) (n=124) revealed little difference between the age groups regarding their involvement in the treatment decision making process. Both groups were more likely than not to have a healthcare provider (HCP) review their pathology report with them (71.0% of U50 vs. 73.2% of 70+); be included in the treatment decision making process (87% of U50 group vs. 86.1% of 70+) and be provided with information before treatment (72.5% of U50 vs.75.4% of 70+). The above findings are all significant at less than 0.001. In making their decision, both groups (75% of U50 vs. 80% of 70+) reported that “Efficacy of Treatment” was their primary consideration. Age should not be a factor in whether to include patients in the treatment decision-making process. Patients, whether under 50, or over 70, can be equally involved in this process, if they choose to be. Communication between the HCP and the patient during the treatment decision-making process may 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. Does Age Impact Patient Involvement in Treatment Decision Making: Results from the Canadian Breast Cancer Network (CBCN) Assessment Project [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 P4-06-09.
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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".