Global representation of heart failure clinical trial leaders, collaborators, and enrolled participants: a bibliometric review 2000–20
Bibliographic record
Abstract
AIMS: The geographic representation of investigators and participants in heart failure (HF) randomized controlled trials (RCTs) may not reflect the global distribution of disease. We assessed the geographic diversity of RCT leaders and explored associations with geographic representation of enrolled participants among impactful HF RCTs. METHODS AND RESULTS: 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. There were 414 eligible RCTs. Only 80 of 828 trial leaders [9.7%; 95% confidence interval (CI): 7.8-11.8%] and 453 of 4656 collaborators (9.7%; 95% CI: 8.8-10.6%) were from outside Europe and North America, with no change in temporal trends and with greater disparities in large RCTs. The adjusted odds of trial leadership outside Europe and North America were lower with industry funding [adjusted odds ratio (aOR): 0.33; 95% CI: 0.15-0.75; P = 0.008]. Among 157 416 participants for whom geography was reported, only 14.5% (95% CI: 14.3-14.7%) were enrolled outside Europe and North America, but odds of enrolment were 10-fold greater with trial leadership outside Europe and North America (aOR: 10.0; 95% CI: 5.6-19.0; P < 0.001). CONCLUSION: 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 | 0.066 | 0.258 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.110 | 0.157 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".