Abstract P6-16-09: What research questions matters most to patients? Final results of the metastatic breast cancer priority setting partnership
Bibliographic record
Abstract
Abstract Background: Research is fundamental to the management of cancer, however many studies are primarily researcher or industry led, with minimal input and involvement from the people most affected by the outcomes- the patients and caregivers. The James Lind Alliance (JLA) is a not for profit initiative that brings patients, caregivers and clinicians together in priority setting partnerships (PSPs) to determine key priorities in research. Breast cancer remains the most common cancer among women, with an estimated third of women diagnosed developing metastatic disease. With advances in treatment, women are living longer with metastatic breast cancer (MBC), in some cases many years. Objectives: The aims of this study are to utilize the JLA approach to (1) identify the unanswered questions about treatment of MBC from patient and clinical perspectives, and (2) to prioritize those that patients and clinicians agree are the most important. Methods: Following the established JLA approach, MBC patients, caregivers, and health professionals were surveyed to elicit their questions pertaining to MBC. Research questions were generated from the survey responses, and following literature review that the questions were currently not completely answered, an interim prioritization survey was conducted to identify a shortlist of questions to take to a final consensus meeting. Results: One thousand, one-hundred and ninety-four responses were collected from 668 individuals(49% patients; 13% physicians; 9% caregivers; 4% allied health care professionals; 2% patient organization representatives; 23% other), which were refined into 62 unique unanswered research questions. The interim prioritization survey was completed by 174 individuals, and the top 27 questions were taken to a final meeting where MBC patients, caregivers, and health care professionals prioritized all the questions, and reached consensus on the top 10. Conclusion: The top 10 questions cover a wide range of research questions, identified by valuable stakeholders as being priorities. These priorities can be used to fund and inform future MBC research. List of final top 10 ranked prioritiesRankQuestion1What biomarkers or intrinsic features of the tumour can be used to identify response to specific treatments and dosing schedules?2What is the role of immunotherapy for MBC?3How can treatment resistance be delayed and minimized?4What causes (i.e. cellular, genomic) breast cancer cells to metastasize, and what changes allow them to penetrate the blood brain barrier?5What is the right sequence of therapy in MBC?6Does local therapy (radiation or surgery to sites of metastatic disease) improve survival outcomes in MBC?7Is continuous treatment with systemic therapy (including HER2-targeted therapy and chemotherapy) better than intermittent?8Does early palliative care improve outcomes for MBC patients?9What are the best methods of education for patients around treatment options and decision making that can lead to improved patient outcomes?10Can safer, more accurate methods, including blood tests of detecting spread of disease (including following curative treatment) be developed? Citation Format: Nixon NA, Simmons C, Lemieux J, Verma S. What research questions matters most to patients? Final results of the metastatic breast cancer priority setting partnership [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P6-16-09.
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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 | 0.165 | 0.222 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".