Implications Of Race On Cognitive Post-concussion Symptoms And Neurocognitive Performance
Bibliographic record
Abstract
Previously, studies have shown that Black/African American, when compared to their White counterparts, are more likely to present with cognitive related symptoms post-concussion. However, further investigation in a larger cohort is warranted. PURPOSE: Assess race-based differences in symptom scores and cognitive tests in concussed individuals. METHODS: The Federal Interagency Traumatic Brain Injury Research database was used. Data were included for analysis if participants: i) were between 18-25 years old ii) identified as Black/African-American or White iii) sustained a concussion <31 days prior to testing. Two symptom measurements were compared across groups: Brief Symptom Inventory 18 (BSI18, n = 140), and Glasgow Outcome Scale Extended (GOSE, n = 140). Five neurocognitive measurements were compared across groups and to published normative data: Trail Making Test A (TMT A, n = 48) and B (TMT B, n = 48), Controlled Oral Word Association Test (COWAT, n = 208), California Verbal Learning Test (CVLTII, n = 235), Grooved Pegboard Test (GPT, n = 48). RESULTS: On the BSI18 Blacks scored higher than Whites, indicating greater symptoms (p = 0.002). There was no difference in GOSE scores (p = 0.63). Mean scores on neurocognitive tests and the effect size between groups is shown in Table 1. TMT B was higher than normative scores for the White group (p = 0.002). For the COWAT and CVLTII both Black and White groups scored significantly lower than normative (p < 0.001). On the GPT Blacks performed significantly better (p = 0.02), while Whites performed significantly worse (p < 0.001) than normative data. CONCLUSION: Black/African Americans scored worse than Whites on all but one neurocognitive assessment. However, in most measures both concussed Black/African American and Whites scored significantly worse when compared to normative data. Further data are required to understand the reasons for this difference.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".