Reliability and validity of using the Lokomat to assess lower limb joint position sense in people with incomplete spinal cord injury
Bibliographic record
Abstract
BACKGROUND: Proprioceptive sense (knowing where the limbs are in space) is critical for motor control during posture and walking, and is often compromised after spinal cord injury (SCI). The purpose of this study was to assess the reliability and validity of using the Lokomat, a robotic exoskeleton used for gait rehabilitation, to quantitatively measure static position sense of the legs in persons with incomplete SCI. METHODS: We used the Lokomat and custom software to assess static position sense in 23 able-bodied (AB) subjects and 23 persons with incomplete SCI (American Spinal Injury Association Impairment Scale level B, C or D). The subject's leg was placed into a target position (joint angle) at either the hip or knee and asked to memorize that position. The Lokomat then moved the test joint to a "distractor" position. The subject then used a joystick controller to bring the joint back into the memorized target position. The final joint angle was compared to the target angle and the absolute difference was recorded as an error. All movements were passive. Known-groups validity was determined by the ability of the measure to discriminate between able-bodied and SCI subjects. To evaluate test-retest reliability, subjects were tested twice and intra-class correlation coefficients comparing errors from the two sessions were calculated. We also performed a traditional clinical test of proprioception in subjects with SCI and compared these scores to the robotic assessment. RESULTS: The robot-based assessment test was reliable at the hip and knee in persons with SCI (P ≤ 0.001). Hip and knee angle errors in subjects with SCI were significantly greater (P ≤ 0.001) and more variable (P < 0.0001) than in AB subjects. Error scores were significantly correlated to clinical measure of joint position sense (r ≥ 0.507, P ≤ 0.013). CONCLUSIONS: This study shows that the Lokomat may be used as a reliable and valid clinical measurement tool for assessing joint position sense in persons with incomplete SCI. Quantitative assessments of proprioceptive deficits after neurological injury will help in understanding its role in the recovery of skilled walking and in the development of interventions to aid in the return to safe community ambulation.
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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 | 0.003 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".