578 A Cross-Sectional Study of Sex, Race, and Ethnic Representation in Burn Clinical Trials
Bibliographic record
Abstract
Abstract Introduction The demographic proportions of plastic surgery trials approximating real-world disease are not well studied. Judicious trial representation is essential in treatment evaluation across diverse patient populations. Herein, we investigate sex, racial and ethnic disparities in patient enrollment across burn trials. Methods Cross-sectional analysis of participants enrolled in high-quality, reduced risk of bias, randomized controlled trials (RCT) registered on clinicaltrials.gov under the query “burn”. Completed RCTs reporting minimum two demographic groups, employing double masking or greater, with results accessible through registry or publications were included. Trial characteristics (country, site location, year, study phase, masking) and demographic data (sex, race, ethnicity) were collected. The Global Burden of Disease database provided sex-based burn disease burdens. Results The primary outcome was the population-to-prevalence ratio of enrolled female participants. Secondary outcomes included representation of racial and ethnic populations as related to blinding, phase, and study/sponsor locations. Of 546 trials, 41 were included (2919 participants). All reported sex demographics, females comprising 37.02% of all participants (PPR=0.71, 95%CI [0.59,0.82], likely indicating underrepresentation against their empiric disease burden). Only 7 and 9 reported ethnicity and race, respectively, although not comprehensively. Caucasians and Black persons comprised 57.52% and 21.80% of participants, respectively, while only 9.80% had Hispanic/Latino ethnicity. Conclusions Females are likely underrepresented in high-quality, US-registered burn trials, unreflective of their real-world disease burden. Further, severe underreporting of race and ethnicity was noted. It is imperative that future trials collect and report demographic data, namely race and ethnicity, and attempt enrolment of diverse demographics and equitable populations for promotion of study generalizability of efficacy data across relevant populations. Applicability of Research to Practice As there is natural variation in the effect of various medications, treatments, or medical products used amongst different sexes and diverse races due to factors such as differing physiological and genetic characteristics, enrolment of participants reflective of the disease pool studied is essential in investigations of treatments or devices intended for clinical practice. Failure to encompass all populations, due to lack of diversity in undergoing treatments further compounds healthcare disparity.
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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 | 0.090 | 0.185 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".