Abstract 10887: The Under-Representation of Blacks, Females and Elderly in Randomized Controlled Trials of Heart Failure with Preserved Ejection Fraction: Meta-Analyses of Randomized Controlled Trials and Observational Studies
Bibliographic record
Abstract
Introduction: Heart failure with preserved ejection fraction (HFpEF) is particularly prevalent in Blacks, women and elderly. Paradoxically, these patients are generally under-represented in randomized controlled trials (RCTs). We aim to compare the representation of Blacks, females and elderly in RCTs and observational studies of HFpEF therapies. Our secondary objective was to evaluate the secular trends of enrollment of these populations in the RCTs of HFpEF. Methods: We conducted a comprehensive database search of PubMed and EMBASE to identify HFpEF RCTs and HFpEF observational studies. Two independent readers extracted data on the proportions of Blacks, females and the mean age of patients enrolled in these studies. We compared the weighted means of age, proportions of blacks and proportions of females in the RCTs to those of observational studies. We divided the RCTs in three groups (enrollment prior to 2010, 2010-2015 and 2015 to 2020). Finally, we ascertained the secular trend of enrollment of Blacks, females and mean age of patients in the RCTs. Results: Our search yielded 17 RCTs and 9 observational studies. The trials included a total of 19,432 patients. There were marked under-enrollment of Blacks and females in the RCTs compared to the observational studies (Table 1). Furthermore, the weighted mean age of patients with HFpEF was also lower than that in the observational studies. From 1997-2020, the enrollment of females and elderly have increased, however, the enrollment of Blacks remained low in the RCTs of HFpEF. Conclusions: Blacks, women and elderly remain underrepresented in RCTs of HFpEF. Future RCTs of HFpEF should aim to enroll more Blacks, females and elderly so that the benefits of novel therapies can be validated in these special populations with high prevalence of HFpEF.
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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 | 0.085 | 0.208 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.027 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".