The prevalence of Black/African American individuals in concussion literature: a systematic review and meta-analysis
Bibliographic record
Abstract
Introduction: Comprising approximately 13.6% of the United States population, Black/African American individuals are overrepresented in sports associated with a high risk of concussion. However, there has been a notable absence of systematic reviews examining whether concussion literature accurately reflects the participation and experiences of Black/African American individuals. Therefore, this study aims to systematically review the prevalence of Black/African American individuals compared to White individuals diagnosed with concussions in the literature. Methods: A systematic search was performed across four electronic databases: PubMed, MEDLINE (Ovid), Scopus and Web of Science. Articles were searched from inception to January 5, 2022. Prevalence data were extracted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A meta-analysis of proportions was conducted within hospital records and national survey data. Results: Among 447 identified studies, 11 were included, representing 1,839,901 individuals diagnosed with a concussion, with 73.6% identifying as White and 12.5% identifying as Black/African American. The mean proportion of Black/African American diagnosed with a concussion in hospital records (13.9%; 95% CI [12.8, 15.1]) exceeded that in national surveys (6.4%; 95% CI [3.5, 11.3]) but lower than sports-centered studies (16%). Discussion: These findings underscore the need to address racial disparities in healthcare within the broader context of social determinants of health and systemic inequities. By identifying gaps in the current research, this study lays the foundation for future investigation aimed at elucidating and addressing healthcare disparities.
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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 | 0.024 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.025 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".