Minorities Are Under-Represented in SLE Clinical Trials: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Objectives With numerous emerging treatments for systemic lupus erythematosus (SLE), populations in trials should reflect the diversity of those affected by SLE so trials are generalizable and are fair/equitable. Data from some North American and European cohorts show that the prevalence of SLE in Black populations can be higher than White populations relative to background population ethnicities.[1,2] 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, or did not report on race/ethnicity. Revman 5.4 and SPSS were used for statistical analysis. Results The literature review identified 2,505 citations, of which 63 were included in the analysis (Figure 1). Of the trials screened for analysis, 39.8% did not report on race, and 12.4% had a sample size less than 50 participants. The proportion of females in the included studies was 91% [95% CI 90-92]. White patients constituted 61% [55-66] of trial participants, while Black participants represented 14% [9-18] (Figure 1). Asians accounted for 14% [12-17], and Indigenous patients 8% [6-10]. Hispanics 37% [31-43], no Pacific Islanders were included [0-0%], and patients of mixed race (by the definition within each study) constituted 3% [2-5]. None of the included studies reported on smoking status, education, marital status, or employment status. Figure 1. Pooled racial makeup of SLE RCTs for black participants Conclusion Standardized reporting of SES surrogates should occur (ie, education, household income). Minorities seem under-represented in RCTs. Greater effort is needed to ensure that SLE research trials are generalizable to patients and are equitable to reflect the diversity of those living with SLE. [1.] Falasinnu T. Curr Rheumatol Rep 2018;20. [2.] Ginzler E. Arthritis Rheumatol 2022;74:112-23.
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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 | 0.063 | 0.151 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.032 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| 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".