Robotic Assessment of Sensorimotor Deficits After Traumatic Brain Injury
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Robotic technology is commonly used to quantify aspects of typical sensorimotor function. We evaluated the feasibility of using robotic technology to assess visuomotor and position sense impairments following traumatic brain injury (TBI). We present results of robotic sensorimotor function testing in 12 subjects with TBI, who had a range of initial severities (9 severe, 2 moderate, 1 mild), and contrast these results with those of clinical tests. We also compared these with robotic test outcomes in persons without disability. METHODS: For each subject with TBI, a review of the initial injury and neuroradiologic findings was conducted. Following this, each subject completed a number of standardized clinical measures (Fugl-Meyer Assessment, Purdue Peg Board, Montreal Cognitive Assessment, Rancho Los Amigos Scale), followed by two robotic tasks. A visually guided reaching task was performed to assess visuomotor control of the upper limb. An arm position-matching task was used to assess position sense. Robotic task performance in the subjects with TBI was compared with findings in a cohort of 170 person without disabilities. RESULTS: Subjects with TBI demonstrated a broad range of sensory and motor deficits on robotic testing. Notably, several subjects with TBI displayed significant deficits in one or both of the robotic tasks, despite normal scores on traditional clinical motor and cognitive assessment measures. DISCUSSION AND CONCLUSIONS: The findings demonstrate the potential of robotic assessments for identifying deficits in visuomotor control and position sense following TBI. Improved identification of neurologic impairments following TBI may ultimately enhance rehabilitation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".