Relationship Between Cognition and Gait at 2- and 12-Months Post-Traumatic Brain Injury
Bibliographic record
Abstract
Background: A common and debilitating challenge experienced by people with TBI is gait-associated mobility impairment and persisting cognitive impairments. Cognitive and physical impairments are often addressed independently during rehabilitation, however, increasing evidence links cognitive and motor processes more closely. Objectives: (1) To determine if correlations exist between measures of cognitive and gait recovery, post-TBI. (2) To investigate the predictive power of cognition at 2-months on gait outcomes at 12-months post-TBI. Methods: In this secondary, longitudinal study of cognitive and neural recovery, data from 93 participants admitted to an inpatient neurorehabilitation program were analyzed. Spatiotemporal gait variables [velocity, step time variability (STV), step length variability (SLV)] were collected along with cognitive variables [Trail Making Test-B (TMT-B), Digit Span-Forward (DS-F)]. Spearman's correlation coefficients were calculated between gait and cognitive variables. Multilinear and step wise regression analyses were calculated to determine predictive value of cognitive variables at 2-months on gait performance at 12-months-post TBI. Results: At 2-months post-injury, TMT-B was significantly correlated with gait velocity and STV; and DS-F was significantly correlated with velocity. At 12-months post-injury, TMT-B and DS-F was still significant correlated with velocity. TMT-B at 2-months was correlated with SLV and STV at 12-months; and DS-F correlated significantly with velocity. Regression models showed TMT-B at 2-months predicting STV, SLV, and velocity at 12-months. Conclusions: Significant associations and predictions between physical and cognitive recovery post-TBI were observed in this study. Future directions may consider a “neural internetwork” model as a salient rehabilitation approach in TBI that integrates physical and cognitive functions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".