Abstract WP89: Evaluating Age, Sex, And Racial / Ethnic Representation In Acute Ischemic Stroke Trials, 2010-2020: A Systematic Review And Meta-Analysis
Bibliographic record
Abstract
Introduction: Enrollment of populations that bear the real-world burden of ischemic strokes is critical to development of generalizable and clinically relevant randomized clinical trials (RCTs). Despite well-recognized historical patterns of underrepresentation for women, older adults, and certain racial/ethnic minorities, major knowledge gaps remain regarding representation in modern trials. We aimed to determine representativeness by age, sex, and race/ethnicity of participants in acute ischemic stroke RCTs performed in the US or Canada Methods: We conducted a systematic review and meta-analysis in accordance with PRISMA, and prospectively registered with PROSPERO (CRD42021247730). ClincalTrials.gov was searched for ischemic stroke RCTs with acute interventions ( < 72 hours following stroke onset) published between 2010-2020. Our search was focused on the published text and supplementary materials of phase 2 and phase 3 trials among adult populations, with ≥5 sites, ≥80% of sites located in the US or Canada, and ≥100 participants. Meta-analysis was based on a random intercept logistic regression model, and confidence intervals (CIs) were derived using the Clopper-Pearson method. Results: Of 548 screenings, 12 RCTs met eligibility criteria (total: 7955 participants). Age and sex were reported by all assessed trials, but race/ethnicity was only documented by 42% (5 publications). Only 33% (4 publications) provided a level of granularity beyond white/non-white for race. Relatively proportionate representation was observed by age (12 studies: heterogeneity in reporting precluded meta-analysis) and sex (12 studies: 48% female, CI 45-51%; I 2 =70%). Black and Asian participants accounted for 19% (CI 12-28%) and 2% (CI 1-4%) of reporting trials’ populations, respectively (4 studies; I 2 =96% and 45%). Hispanic participants encompassed 11% of reporting trials’ populations (CI 8-15%; 5 studies; I 2 =97%). Conclusions: While age and sex were routinely documented, less than half of the assessed US or Canadian acute ischemic stroke RCTs reported on race/ethnicity. We recommend further emphasis on complete and granular reporting of racial/ethnic profiles in order to facilitate ongoing assessments of representation among modern stroke trials.
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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 | 0.052 | 0.114 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.026 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".