Abstract 9476: Global Representation of Leaders, Collaborators, and Enrolled Participants in Heart Failure Clinical Trials: A Systematic Bibliometric Review
Bibliographic record
Abstract
Introduction: The geographic representation of investigators and participants in heart failure (HF) randomized clinical trials (RCTs) may not reflect the global burden of disease. Objectives: We assessed the geographic diversity of RCT leaders and explored associations with the geographic representation of enrolled participants among impactful HF RCTs. Methods: We searched MEDLINE, EMBASE, and CINAHL for HF RCTs published in journals with impact factor ≥ 10 between January 2000 and June 2020. We used the Jonckheere-Terpstra test to assess temporal trends and multivariable logistic regression models to explore associations between predictors and outcomes. Results: There were 414 eligible RCTs. Only 80 of 828 trial leaders (9.7%; 95% CI: 7.8% to 11.8%), and 453 of 4656 collaborators (9.7%; 95% CI: 8.8% to 10.6%) were from outside Europe and North America, with no change in temporal trends. The adjusted odds of trial leadership outside Europe and North America were lower with industry funding (aOR: 0.33; 95% CI: 0.15 to 0.75; P = 0.008). Among 157,416 participants in whom geography was reported, only 14.5% (95% CI: 14.3% to 14.7%) were enrolled outside Europe and North America, but odds of enrolment were ten-fold greater with trial leadership outside Europe and North America (aOR: 10.0; 95% CI 5.6-19.0; P < 0.001). Conclusions: Regions disproportionately burdened with HF are under-represented in HF trial leadership, collaboration, and enrolment. RCT leadership outside Europe and North America is independently associated with participant enrolment in under-represented regions. Increasing research capacity outside Europe and North America could enhance trial diversity and generalizability.
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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 | 0.064 | 0.266 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.008 |
| Bibliometrics | 0.085 | 0.104 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| 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".