Using Robotics to Quantify Impairments in Sensorimotor Ability, Visuospatial Attention, Working Memory, and Executive Function After Traumatic Brain Injury
Bibliographic record
Abstract
OBJECTIVE: To investigate the use of a robotic assessment tool to quantify sensorimotor, visuospatial attention, and executive function impairments in individuals with traumatic brain injury (TBI). SETTING: Foothills Hospital (Calgary, Canada). PARTICIPANTS: Twenty-three subjects with first-time TBI in the subacute to chronic phase participated in this study. Normative data were collected from 275 to 494 neurologically intact control subjects for each robotic task. DESIGN: A prospective observational case series. Subjects with TBI completed brief clinical cognitive and motor assessments followed by robotic assessments of upper limb reaching, position sense, bimanual motor ability, attention, and visuospatial skills. Scores of subjects with TBI were compared with normative data. MAIN MEASURES: Robotic task performance was computed for each subject on each task, as well as performance on specific task parameters. Clinical assessments included the Montreal Cognitive Assessment, Fugl-Meyer upper extremity assessment, and Purdue Peg Board. RESULTS: Subjects with TBI demonstrated a variety of deficits on robotic tasks. The proportion of TBI subjects who were significantly different from controls ranged from 36% (dominant arm reaching) to 60% (bimanual object hitting task). CONCLUSION: Robotic measures allowed us to quantify a range of impairments specific to each subject, and offer an objective tool with which to examine these abilities after 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 | 0.002 |
| 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".