Diversity in enrollment to clinical trials for cataract medicine and surgery: meta-analysis
Bibliographic record
Abstract
PURPOSE: To investigate sex, racial, and ethnic disparities in patient enrollment across cataract trials registered in the United States. SETTING: Participants enrolled in high-quality (reduced risk of bias), U.S.-registered (on ClinicalTrials.gov ), cataract-related randomized controlled trials (RCTs). RCTs must be completed, have used double or greater masking, and have published results through the registry or a scholarly journal. DESIGN: Cross-sectional database study. METHODS: Trial (study sponsor country, study site location, trial initiation year, study phase, and study masking) and demographic data (sex, race, and ethnicity according to U.S. reporting guidelines) were collected. The Global Burden of Disease database provided sex-based cataract disease burdens. Pooled participation-to-prevalence ratios (PPRs) with 95% CIs were calculated for female sex, with values between 0.8 and 1.2 constituting sufficient study enrollment. Kruskal-Wallis tests (α = 0.05) with subsequent post hoc comparisons were used to evaluate demographic representations stratified by trial characteristics. RESULTS: From 864 records, 100 clinical trials (N = 67 874) were identified, of which 97 (N = 67 697) reported sex demographics with a pooled female PPR of 0.89 (95% CI, 0.85-0.94). Of the 67 697 total participants, the absolute female enrollment was 19 062 (28.16%). Ethnicity and race were reported in 9 (N = 1792) and 26 trials (N = 23 181), respectively. Among trials that reported race, most were White (N = 19 574; 84.44%). CONCLUSIONS: High-quality, U.S.-registered, cataract trials enrolled acceptable proportions of women. However, the absolute number of female and racialized participants was low. Race and ethnicity were underreported. Disparity trends predominately held across secondary variables. To promote generalizability, future trials should pursue equitable demographic enrollment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.128 | 0.360 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.027 | 0.014 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".