The representation of Indigenous peoples in chronic disease clinical trials in Australia, Canada, New Zealand, and the United States
Bibliographic record
Abstract
BACKGROUND: Indigenous peoples are overrepresented with chronic health conditions and experience suboptimal outcomes compared with non-Indigenous peoples. Genetic variations influence therapeutic responses, thus there are potential risks and harm when extrapolating evidence from the general population to Indigenous peoples. Indigenous population-specific clinical studies, and inclusion of Indigenous peoples in general population clinical trials, are perceived to be rare. Our study (1) identified and characterized Indigenous population-specific chronic disease trials and (2) identified the representation of Indigenous peoples in general population chronic disease trials conducted in Australia, Canada, New Zealand, and the United States. METHODS: For Objective 1, publicly available clinical trial registries were searched from May 2010 to May 2020 using Indigenous population-specific terms and included for data extraction if in pre-specified chronic disease. For identified trials, we extracted Indigenous population group identity and characteristics, type of intervention, and funding type. For Objective 2, a random selection of 10% of registered clinical trials was performed and the proportion of Indigenous population participants enrolled extracted. RESULTS: In total, 170 Indigenous population-specific chronic disease trials were identified. The clinical trials were predominantly behavioral interventions (n = 95). Among general population studies, 830 studies were randomly selected. When race was reported in studies (n = 526), Indigenous individuals were enrolled in 172 studies and constituted 5.6% of the total population enrolled in those studies. CONCLUSION: Clinical trials addressing chronic disease conditions in Indigenous populations are limited. It is crucial to ensure adequate representation of Indigenous peoples in clinical trials to ensure trial data are applicable to their clinical care.
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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 | 0.140 | 0.287 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".