Longitudinal Recovery of Executive Control Functions After Moderate-Severe Traumatic Brain Injury: Examining Trajectories of Variability and Ex-Gaussian Parameters
Bibliographic record
Abstract
BACKGROUND: Executive control deficits are deleterious and enduring consequences of moderate-severe traumatic brain injury (TBI) that disrupt everyday functioning. Clinically, such impairments can manifest as behavioural inconsistency, measurable experimentally by the degree of variability across trials of a reaction time (RT) task (also known as intraindividual variability [IIV]). Growing research on cognition after TBI points to cognitive deterioration in the chronic stages postinjury. OBJECTIVE: To examine the longitudinal recovery of RT characteristics (IIV and more detailed ex-Gaussian components, as well as the number of impulsively quick responses) following moderate-severe TBI. METHODS: Seventy moderate-severe TBI patients were assessed at 2, 5, 12, and 24+ months postinjury on a go/no-go RT task. RT indices (ex-Gaussian parameters mu and sigma [mean and variability of the normal distribution component], and tau [extremely slow responses]; mean, intraindividual coefficient of variation [ICV], and intraindividual standard deviation [ISD]) were analyzed with repeated-measures multivariate analysis of variance. RESULTS: ICV, ISD, and ex-Gaussian tau significantly decreased (ie, improved) over time in the first year of injury, but worsened from 1 to 2+ years, as did the frequency of extremely fast responses. These quadratic patterns were accentuated by age and shown primarily in tau (extremely slow) and extremely fast (impulsive) responses. CONCLUSIONS: The pattern of early recovery followed by decline in executive control function is consistent with growing evidence that moderate-severe TBI is a progressive and degenerative disorder. Given the responsiveness to treatment of executive control deficits, elucidating the trajectory and underpinnings of inconsistent behavioral responding may reveal novel prognostic and clinical management opportunities.
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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 | 0.002 |
| 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 | 0.001 |
| 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".