MINORITIES ARE Underrepresented IN SLE CLINICAL TRIALS: A SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
PV194 / #729 Poster Topic: AS22 - SLE Heterogeneity Background/Purpose With numerous emerging treatments for systemic lupus erythematosus (SLE), it is crucial that populations in trials reflect the diversity of those affected by SLE. Data from North American and European cohorts show that the of SLE in prevalence of Black populations can be higher than White populations in some locations relative to background population ethnicities. The objectives of this study were to examine the racial composition of clinical trials in SLE from 2014 to 2024, assessing whether these trials accurately represent the diversity of SLE populations. Methods A systematic review of the literature was conducted using EMBASE, PUBMED, Web of Science, and Cochrane CENTRAL from Jan 1, 2014 – May 14, 2024. Randomized trials of pharmaceutical interventions in SLE patients were included. Studies were excluded if they had less than 50 participants, were not in English, did not report on race/ethnicity. Revman 5.4 and SPSS were used for statistical analysis. Results 2505 studies were identified, 63 were included. The pooled proportion of women was 91%. Caucasians represented 61% of those included in trials compared to only 14% of Blacks, 37% of Hispanics and Asians only 14%. Indigenous patients represented 8% whereas none were Pacific Islanders. There was a paucity of information on educational status, income, employment, urban/rural address, or marital status. Conclusions Standardized reporting of SES surrogates should occur (ie, education, household income). Minorities seem underrepresented in RCTs. Greater effort is needed to ensure that SLE research trials are generalizable to patients and equitable with respect to patient diversity.
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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 | 0.052 | 0.137 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.030 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".