Diversity in Axial Spondyloarthritis Drug Trials: Enrollment by Sex, Race, Ethnicity, and Geographic Region
Bibliographic record
Abstract
OBJECTIVE: To examine demographic and geographic diversity in axial spondyloarthritis (axSpA) drug trials. METHODS: We performed a descriptive epidemiological study using ClinicalTrials.gov data. We included completed phase II-IV drug trials in adults with axSpA, conducted between 2000 and 2023, with results posted on ClinicalTrials.gov. We extracted and analyzed data on sex, race, ethnicity, trial characteristics, and trial locations. RESULTS: Fifty-nine trials with 16,162 participants were analyzed. Female individuals constituted 30% of participants overall: 25% in radiographic axSpA (r-axSpA) trials, 34% in axSpA trials, and 48% in nonradiographic axSpA (nr-axSpA) trials. Thirty-one trials (53%) reported race, and 12 (20%) reported both race and ethnicity. Race reporting increased from 9% of trials (2000-2010) to 53% (2011-2015) and 100% (2016-2020). Among 10,037 participants with race data, 82% were White, 15% Asian, 2% American Indian/Alaska Native, 1% Black, and 0.02% Native Hawaiian/Pacific Islander. Asian representation increased from 4% (2011-2015) to 19% (2016-2020) and American Indian/Alaska Native from 1% to 3%, whereas Black representation remained consistently low at 1%. Among 3577 patients with ethnicity data, 14% of participants were Hispanic/Latino, increasing from 1% (2011-2015) to 14% (2016-2020). Fifty-one trials with location data enrolled participants from 53 countries. Sub-Saharan Africa (0%) and South/Central Asia (2%) had the lowest geographic representation of enrollment sites. CONCLUSION: The proportion of women enrolled in axSpA drug trials largely reflects disease demographics. Race and ethnicity reporting has improved over time. Whereas participation of Asian, American Indian/Alaska Native and Hispanic/Latino patients has increased, Black and Native Hawaiian/Pacific Islander representation has remained low. Future efforts should prioritize inclusivity and participation in underrepresented regions globally.
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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 | 0.019 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".