Advancing equitable access to innovation in breast cancer
Bibliographic record
Abstract
This manuscript critically examines the challenges associated with the design and conduct of academic global breast cancer trials outside the influence of pharmaceutical companies, leveraging insights from the Breast International Group (BIG). In the past 4 decades significant declines in breast cancer mortality have occurred, partly related to industry-academic clinical and translational partnerships with long term study follow up. However, in the past decade these partnerships have largely uncoupled. The increasing complexity and non-alignment of trials, funding constraints, regulatory complexity, declining academic freedom, lack of transparency, and lack of affordability of new agents have become key barriers to equitably improving cancer outcomes. Industry research expenditure in the United States is now 5 fold greater than publically funded academic research. To address these challenges, we advocate for patient centred systemic reforms, with trials balancing commercial interests with public health imperatives. These reforms should include equitable research funding models, streamlined international clinical trial regulatory processes, and increased collaboration across diverse stakeholders. Practical solutions to enhance global trial accessibility and efficacy include leveraging digital technologies, artificial intelligence, real world data, decentralizing clinical trial infrastructure, and embedding translational research frameworks across countries.
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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 | 0.117 | 0.228 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 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".