Information processing difficulty long after self-reported concussion
Bibliographic record
Abstract
Recovery of cognitive function after mild head injury (MHI) is thought to be relatively swift and complete. The present study replicates and extends previous work in which university students with self-reported concussion demonstrated reduced P300 amplitude on a set of easy and difficult attention tasks, in addition to performing more poorly than controls on demanding cognitive tasks many years after injury. In the present study, 13 students with self-reported concussion (MHI group: M time since injury = 8 years) and 10 controls were matched for age, sex, education, and a variety of cognitive, physical and emotional complaints. Controls outperformed the MHI group on the Digit Symbol substitution task and on a difficult dual task involving tone discrimination and visual working memory. Additionally, controls exhibited larger P300 amplitudes on both an easy and a difficult auditory discrimination task. A combination of electrophysiological, neuropsychological and self-report indices predicted group membership (MHI vs. control) with 88% accuracy. The present results, coupled with previous work, offer preliminary evidence that the combination of event-related potentials and demanding behavioral measures might reveal long-lasting, subtle cognitive problems associated with MHI. These findings may challenge existing notions of complete recovery after MHI.
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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 | 0.000 | 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 | 0.001 | 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".