Advancing Inclusive Research (AIR) Site Alliance: Facilitating the inclusion of historically underrepresented people in oncology and ophthalmology clinical research
Bibliographic record
Abstract
BACKGROUND: The Advancing Inclusive Research (AIR) Site Alliance is composed of clinical research centers that partner with Genentech, a biotechnology company, to advance the representation of diverse patient populations in its oncology and ophthalmology clinical trials, test recruitment, and retention approaches and establish best practices to leverage across the industry to achieve health equity. METHODS: Through a data-driven selection process, Genentech identified 6 oncology and 3 ophthalmology partners that focus on reaching historically underrepresented patients in clinical trials and worked collaboratively to share knowledge and explore original ways of increasing clinical study access for every patient, including sites co-creation of a Protocol Entry Criteria Guideline with inclusion principles. RESULTS: For patients, three publicly available educational videos about clinical trials were created in multiple languages. The AIR Site Alliance has also defined invoiceable services for sites to enhance patient support; this has been built into the new study budget templates for sustainability. For healthcare professionals (HCPs), the first-of-its-kind AIR Educational Program was developed to focus on identifying and addressing bias and engaging historically underrepresented patient populations in trials. The sites also co-created videos for HCPs and patients on why advancing inclusive research matters. Over 16 regional health equity symposia have been delivered for patients, HCPs, and community leaders. CONCLUSIONS: This AIR Site Alliance is a model for other site alliances, including Kenya, South Africa, the United Kingdom, and Canada. Such alliances will build a robust and sustainable research ecosystem that includes diverse patient groups and encourages change across the healthcare system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.567 | 0.814 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.014 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads 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".