Global representation of heart failure clinical trial leaders and collaborators: a systematic bibliometric review 2000–2020
Bibliographic record
Abstract
Abstract Aims Heart Failure (HF) has a disproportionate burden in low- and middle-income countries. The geographic representation of those who lead HF randomized clinical trials (RCTs) may not reflect the geographic burden of disease. We assessed temporal trends and trial characteristics associated with leadership outside Europe and North America, and explored whether there was a geographic association between trial leadership and participant enrolment. Methods and results We searched MEDLINE, EMBASE, and CINAHL for HF RCTs published in journals with an impact factor ≥10 between January 1, 2000, and June 17, 2020. We used the Jonckheere-Terpstra test to assess temporal trends and multivariable logistic regression models to determine associations between predictor and outcome variables. 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 regions outside Europe and North America, with no temporal change in geographic representation. The odds of trial leadership outside Europe and North America were significantly lower with industry versus public funding (OR: 0.33; 95% CI: 0.15 to 0.75; P=0.008). Trial leadership outside Europe and North America was associated with enrolment of patients outside Europe and North America (OR: 10.0; 95% CI 5.6–19.0; P<0.001). Conclusion Trial leadership outside Europe and North America is rare, particularly in industry funded trials, and is associated with participant enrolment in regions with disproportionate disease burden. Building research capacity and networks in under-represented regions could increase generalizability of trial results. Funding Acknowledgement Type of funding sources: None.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.058 | 0.239 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.076 | 0.107 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".