Indigenous peoples and inclusion in clinical and genomic research: Understanding the history and navigating contemporary engagement
Bibliographic record
Abstract
Despite significant improvements in pediatric cancer survival outcomes, there remain glaring disparities in under-represented racial and ethnic groups that warrant mitigation by the scientific and clinical community. To address and work towards eliminating such disparities, the Pacific Pediatric Neuro-Oncology Consortium (PNOC) and Children's Brain Tumor Network (CBTN) established a Diversity, Equity, and Inclusion (DEI) working group in 2020. The DEI working group is dedicated to improving access to care for all pediatric patients with central nervous system (CNS) tumors, broadening diversity within the research community, and providing sustainable data-driven solutions. To this end, the DEI working group aims to coordinate regular educational sessions centered on critical DEI topics in pediatric research and clinical care of pediatric patients, with a focus on pediatric neuro-oncology. In April 2022, the group led a moderated panel of experts on Indigenous Peoples' rights and participation in clinical research activities. The following paper serves to provide the scientific community a perspective on how to prioritize the inclusion of Indigenous Peoples in research with cultural sensitivity and with the intent of improving not only representation, but patient outcomes regardless of patient race, ethnicity, or socioeconomic background.
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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 | 0.092 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.049 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".