Can measures of cognitive function predict locomotor behaviour in complex environments following a traumatic brain injury?
Bibliographic record
Abstract
PRIMARY OBJECTIVE: To determine the relationships between clinical measures of executive function and attention, and laboratory measures of anticipatory locomotor adaptations with dual tasks following a TBI. METHODS AND PROCEDURES: Ten people with moderate or severe TBI were compared to 10 healthy subjects for neuropsychological measures in the clinic, as well as locomotor patterns and reading time in the laboratory for adapted Stroop tasks (Bar and Word) during unobstructed and obstructed walking. MAIN OUTCOMES AND RESULTS: As previously found 1 (Vallee M, McFadyen BJ, Swaine B, Doyon J, Cantin JF, Dumas D. Effects of environmental demands on locomotion after traumatic brain injury. Archives of Physical Medicine Rehabilitation 2006;87:806--813) during the locomotor activities, subjects with TBI walked slower, had higher clearance margins and took longer to read during the Stroop tasks than healthy subjects. In general, subjects with TBI also showed deficits in executive functions and attention. Significant relationships were specifically observed between scores on Trail Making B and clearance margins for subjects with TBI, but not for healthy subjects. Alternatively, significant relationships between clinical scores on Stroop and dual task Stroop reading times were obtained for healthy subjects but not for subjects with TBI. CONCLUSIONS: These results suggest that measures of executive functioning and attention may be associated to locomotor behaviour in complex environments following a moderate to severe TBI.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 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".