Determining research priorities for young women with breast cancer: A priority setting partnership
Bibliographic record
Abstract
BACKGROUND: Women under the age of 40 years account for approximately 5-7% of breast cancer cases. Young women with breast cancer (YWBC) often have a biologically distinct disease and unique considerations compared to older women, leading to significant opportunities for research to improve patient outcomes and quality of life. Potential disconnects between research being performed and that deemed meaningful and relevant to patients may impede clinical uptake and knowledge translation. The James Lind Alliance (JLA) brings patients, caregivers and clinicians together in priority setting partnerships (PSPs) to determine key priorities in health research. OBJECTIVE: To determine the top-10 research priorities for YWBC using an established, multi-stakeholder, priority setting methodology. METHODS: An adapted JLA PSP process was used to determine research priorities for YWBC. A balanced, 15-member steering committee composed of patients, caregivers and clinician representatives from across Canada was created to coordinate and implement the activities of the PSP including the creation, approval and dissemination of a nation-wide survey. Survey responses were received in text-based format, sorted and separated into themes, and eventually organized into scientific questions. A raw list of potential uncertainties underwent a reduction process whereby duplicates were combined and out of scope questions were removed. The steering committee voted on a shortlist of 50 questions to be used in the interim prioritization survey. The interim prioritization survey was disseminated back to participants and a reverse ranking process was used to identify the top-30 priorities brought to the final consensus meeting. The final meeting took place through an interactive, virtual, one-day workshop using a nominal group technique with patients, caregivers, clinicians and other stakeholders from across Canada and culminated in consensus on the top-10 priorities for YWBC. RESULTS: One thousand, four hundred and twelve responses were generated from 359 respondents (75% patient generated). A raw list of 423 unique questions were developed once out of scope (n=278) and repeat questions (n=711) were eliminated. Of the remaining questions, 209 were deemed to be gaps in knowledge pertaining specifically to YWBC while the remainder were questions about breast cancer in general. This list was reduced through iterative voting to a Top-50 by the steering committee, Top-30 through the interim prioritization survey and eventual a Top-10 through a final consensus meeting. The priority areas identified include: surgical outcomes, Tamoxifen duration, effect of interruption to hormone blockade, biological markers to detect microscopic disease, long-term consequences of treatment induced menopause, lifestyle and modifiable factors to reduce recurrence, prediction tools for identifying young women at high risk of developing breast cancer, the impact of inflammatory and autoimmune diseases on developing breast cancer in the young, indications for more aggressive treatment and quality improvement to reduce the interval period between diagnostic treatment and diagnosis. CONCLUSION: This work is part of an integrative knowledge translation strategy in partnership with the RUBY study and will inform future research moving forward. Additionally, it be of interest to the broader early onset breast cancer community as well as funding agencies.
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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 | 0.254 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.004 | 0.034 |
| Research integrity | 0.005 | 0.010 |
| 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; 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".