Factors Associated With Racial and Ethnic Diversity Among Heart Failure Trial Participants: A Systematic Bibliometric Review
Bibliographic record
Abstract
Background: Heart failure has a disproportionate burden on patients who are Black, Indigenous, and people of color (BIPOC), but not much is known about representation of these groups in randomized controlled trials (RCTs). We explored temporal trends in and RCT factors associated with the reporting of race and ethnicity data and the enrollment of BIPOC in heart failure RCTs. Methods: We searched MEDLINE, EMBASE, and CINAHL for heart failure RCTs published in journals with an impact factor ≥10 between January 1, 2000 and June 17, 2020. We used the Cochran-Armitage and Jonchkeere-Terpstra tests to examine temporal trends, and multivariable regression to assess the association between trial characteristics and outcomes. Results: Of 414 RCTs meeting inclusion criteria, only 157 (37.9% [95% CI, 33.2%–2.8%]) reported race and ethnicity data. Among 158 200 participants in these 157 RCTs, 29 512 (18.7% [95% CI, 18.5%–18.9%]) were BIPOC. There was a temporal increase in reporting of race and ethnicity data (29.5% in 2000–2003 to 54.7% in 2016–2020, P <0.001) and in enrollment of BIPOC (14.4% in 2000–2003 to 22.2% in 2016–2020, P =0.038). Trial leadership by a woman was independently associated with twice the odds of reporting race and ethnicity data (odds ratio, 2.0 [95% CI, 1.1–3.8]; P =0.028) and an 8.4% increase (95% CI, 1.9%–15.0%; P =0.013) in BIPOC enrollment. Conclusions: A minority of heart failure RCTs reported race and ethnicity data, and among these, BIPOC were under-enrolled relative to disease distribution. Both reporting of race and ethnicity as well as enrollment of BIPOC increased between 2000 and 2020. After multivariable adjustment, trials led by women had greater odds of reporting race and ethnicity and enrolling BIPOC. Registration: URL: https://www.crd.york.ac.uk/PROSPERO/ ; Unique identifier: CRD42021237497.
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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 | MetaresearchBibliometrics Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Systematic review | 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 | 0.037 | 0.186 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.039 | 0.055 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| 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, 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".