Returning value to communities from the <i>All of Us</i> Research Program through innovative approaches for data use, analysis, dissemination, and research capacity building
Bibliographic record
Abstract
In 2015, the White House and the National Institutes of Health announced the inception of the All of Us Research Program “to bring us closer to curing diseases like cancer and diabetes, and to give each of us access to the personalized information we need to keep ourselves and our families healthier.” Now approaching the first decade, the vision to enhance innovation in biomedical research remains strong with the goal of moving the United States into an era where medical treatment and other health interventions can be tailored to individuals. As of October 2024, there are over 842 000 participants who have consented to participate in the All of Us Research Program. The resulting data set, which is accessed through the All of Us Public Data Brower (aggregated data only) or Researcher Workbench reflects three novel aspects: (1) enriched enrollment for racial, ethnic, sexual, gender, and geographic minority populations to correct for past sampling bias in precision medicine studies, (2) inclusion of social determinants of health (SDoH), electronic health record (EHR), and genomic data, and (3) designed for use by scientists with a broad variety of backgrounds and different research (eg, research-intensive universities, community-based organizations) and educational settings (eg, Historically Black Colleges and Universities, high schools).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.221 | 0.337 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.029 | 0.026 |
| Open science | 0.006 | 0.050 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".