The Breast International Group (BIG) Patient Partnership: Embedding meaningful patient involvement in the design and conduct of breast cancer clinical research
Bibliographic record
Abstract
Involving those with a lived experience of the relevant condition in the design of clinical research ensures that studies address real-world needs and priorities, enabling more relevant, ethical, and impactful outcomes. In 2019, the Breast International Group (BIG) established the BIG Patient Partnership to facilitate the meaningful involvement of people affected by breast cancer in the design and conduct of its studies. The members provide a strong international patient voice in academic breast cancer research. The partnership is based on 4 pillars: foundational and ongoing training, meaningful and systematic involvement, patients as a strategic driving force in BIG's research, and promoting the value of the patients' voice in research. In this paper, we describe a model to enable performing transnational clinical research for and with patient partners. We hope to inspire organizations and people who are burdened by cancer from different cultural backgrounds to develop an interactive, engaging, and empowering process for researchers and patient partners to work together.
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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 | 0.235 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.024 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.003 | 0.049 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".