Attention following traumatic brain injury: Neuropsychological and driving simulator data, and association with sleep, sleepiness, and fatigue
Bibliographic record
Abstract
The objectives of this study were to compare individuals with traumatic brain injury (TBI) and healthy controls on neuropsychological tests of attention and driving simulation performance, and explore their relationships with participants' characteristics, sleep, sleepiness, and fatigue. Participants were 22 adults with moderate or severe TBI (time since injury ≥ one year) and 22 matched controls. They completed three neuropsychological tests of attention, a driving simulator task, night-time polysomnographic recordings, and subjective ratings of sleepiness and fatigue. Results showed that participants with TBI exhibited poorer performance compared to controls on measures tapping speed of information processing and sustained attention, but not on selective attention measures. On the driving simulator task, a greater variability of the vehicle lateral position was observed in the TBI group. Poorer performance on specific subsets of neuropsychological variables was associated with poorer sleep continuity in the TBI group, and with a greater increase in subjective sleepiness in both groups. No significant relationship was found between cognitive performance and fatigue. These findings add to the existing evidence that speed of information processing is still impaired several years after moderate to severe TBI. Sustained attention could also be compromised. Attention seems to be associated with sleep continuity and daytime sleepiness; this interaction needs to be explored further.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".