Gender and race in neurotrauma: part 2–underrepresentation in clinical trial enrollment and impact on clinical outcomes
Bibliographic record
Abstract
The underrepresentation of women and racial minorities in clinical trials populations remains a persistent challenge across many medical specialties, including Neurosurgery. A diverse research cohort brings varied perspectives and experiences, which can lead to more innovative solutions to medical problems, generalizable findings, and the foundations to provide culturally competent care to the populations most affected by the condition at hand. The importance of representative Neurotrauma trial populations cannot be overstated, as results are essential to inform decision making and gender and race have both been shown to significantly influence patient outcomes, as seen in the traumatic brain injury and spinal cord injury populations. Although the path towards gender and racial parity in clinical trial participants has been slow, numerous actions have been taken, including the FDA Safety and Innovation Act (2012) and Omnibus Reform Act (2022) on a systemic level. In this paper, we aimed to explore the barriers to and implications of inadequate representation in neurotrauma trials to outline a roadmap towards more diverse trial inclusion and retention. Key strategies moving forward include recruiting a diverse research team, developing flexible study protocols that support the varying needs of individuals of different backgrounds, establishing methods of data analysis that control for social and demographic factors instead of excluding individuals from participating, introducing patient navigators, reflecting on systemically engrained biases, implementing mandatory reporting of gender and race data, establishing and analyzing policies that keep researchers accountable towards goals of inclusive recruitment, and identifying and addressing unique barriers that individuals at the intersection of gender and racial minority status face.
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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 | 0.142 | 0.287 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".