Randomized Controlled Trial: Preliminary Investigation of the Impact of High-Intensity Treadmill Gait Training on Recovery Among Persons with Traumatic Brain Injury
Bibliographic record
Abstract
Exercise to treat traumatic brain injury (TBI) is a novel approach that has only become recognized in the past decade. High-intensity gait training (HIGT) has been studied in subjects following stroke; however, little research investigates similar protocols on patients with TBI. The study evaluated HIGT as an intervention for enhancing patient recovery after TBI. Adult subjects (18–65 years) who suffered TBI were randomly allocated to an intervention (HIGT) or control (low-intensity physical therapy) group given three days/week for 1 h over four weeks. Assessments included the 10-m walk test, 6-min walk test, Berg Balance Scale, five-times sit-to-stand (5TSTS), timed up and go (TUG), cognitive TUG, and Montreal Cognitive Assessment (MoCA) at day one, two weeks, four weeks, and a four-week follow-up. In addition to a trend toward improved gait speed (p < 0.1) and significantly improved endurance (p < 0.05) in the HIGT group (n = 5), both the control (n = 4) and HIGT groups demonstrated trends toward improved mobility (5TSTS, p < 0.1; TUG, p < 0.1) and significantly improved cognition (cognitive TUG, p < 0.01; MoCA, p < 0.05) over the four-week time period and at the one-month follow-up. HIGT showed longer-lasting rehabilitative effects on gait distance, endurance, mobility, and cognitive function at the four-week follow-up. This study suggests that HIGT may support functional recovery, and future work will involve increasing sample size.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".