Representation of racialised and ethnically diverse populations in multicentre randomised controlled trials of GLP-1 medicines for obesity: a systematic review and meta-analysis of gaps
Bibliographic record
Abstract
INTRODUCTION: Trials of GLP-1 (glucagon-like peptide-1) medicines have changed the paradigm of obesity treatment. Diversity in trial participation is imperative considering that obesity disproportionately impacts marginalised populations worldwide. We performed a systematic review and meta-analyses to evaluate the representation of racialised and ethnically diverse populations in randomised controlled trials (RCTs) of GLP-1 medicines for obesity. METHODS: We searched PubMed/Embase/ClinicalTrials.gov. Prevalence of each racial/ethnic group was compared in relation to the USA, Canada, the UK, Brazil and South Africa. The geographical locations of the trial sites were extracted. RESULTS: 27 RCTs were identified (n=21 547 participants). Meta-analyses of prevalence demonstrated the vast predominance of white/Caucasians (79%) with smaller proportion of blacks (9%), Asians (13%), Indigenous (2%) and Hispanics (22%). The gaps in representation were evidenced by the significantly under-represented proportion of non-white individuals in these RCTs as compared with the prevalence of non-white individuals in the general population of the USA (-23%, p=0.002) and Canada (-34%, p<0.0001), reaching an alarming gap of -58% in relation to Brazil and striking under-representation of -68% as compared with South Africa. Similar discrepancies in proportions of blacks, Asians and Indigenous peoples as compared with reference nations were found. Moreover, the trial sites (n=1859) were predominately located in high-income countries (84.2%), in sharp contrast to the global prevalence of obesity that is predominantly in low-income and middle-income countries. CONCLUSION: There are discrepancies in representation of racialised and ethnically diverse populations in obesity trials as compared with multiethnic populations worldwide. These data highlight the need for broader reform in the research process in order to ultimately address health inequities.
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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 | 0.092 | 0.183 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.037 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".