Education Quality, Reading Recognition, and Racial Differences in the Neuropsychological Outcome from Traumatic Brain Injury
Bibliographic record
Abstract
Ethnically diverse examinees tend to perform lower on neuropsychological tests. The practice of adjusting normative comparisons for the education level and/or race to prevent overpathologizing low scores is problematic. Education quality, as measured by reading recognition, appears to be a more accurate benchmark for premorbid functioning in certain populations. The present study aimed to extend this line of research to traumatic brain injury (TBI). We hypothesized that a measure of reading recognition, the Wechsler Test of Adult Reading (WTAR), would account for racial differences in neuropsychological performance after TBI. Fifty participants (72% African American, 28% Caucasian) with moderate to severe TBI underwent neuropsychological testing at 1-year post-injury. Reading recognition accounted for all the same variance in neuropsychological performance as race and education (together), as well as considerable additional variance. Estimation of premorbid functioning in African Americans with TBI could be refined by considering reading recognition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".