Race-based reporting and participation of Black individuals in registered pain clinical trials, United States, 2000 to 2019
Bibliographic record
Abstract
ABSTRACT: There are numerous, well-established racial disparities in the management of pain. The degree to which these are evident at the stage of conducting clinical trials is unknown. To address this knowledge gap, we examined race-based reporting, participation of Black individuals, and the factors associated with reporting and participation in pain clinical trials in the United States. Data were extracted from Clinicaltrials.gov and published articles. One thousand two hundred trials met our inclusion criteria; 482 (40.2%) reported participant race. More recent, publicly funded, and larger trials were more likely to report race. Of 82,468 participants included in pain clinical trials that reported race, 15,101 were Black individuals (18.3%). Participation of Black individuals was significantly associated with pain type (ß = +27% in cardiovascular disease pain compared with acute pain, P < 0.05), study population (ß = +33% and +7% in pain in minoritized populations and women, respectively, compared with general population, P < 0.05), pain intervention (ß = +7.5% for trials of opioid interventions compared with nonopioid interventions, P < 0.05), and a diverse team of investigators (ß = +8.0% for studies incorporating a visible non-White investigator compared with those that did not, P < 0.05). Our results indicate that representation of Black participants in pain clinical trials generally aligns with national demographics in the United States. Increased representation corresponds with health conditions more prevalent among Black individuals (eg, cardiovascular disease) and with a diverse study team composition. Despite these encouraging results, less than half of pain trials reported race, which introduces potential publication bias and limits external validity.
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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 | 0.056 | 0.179 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".