Surface EMG in Subacute and Chronic Care after Traumatic Spinal Cord Injuries
Bibliographic record
Abstract
Background: Traumatic spinal cord injury (SCI) is a devastating condition commonly originating from motor vehicle accidents or falls. Trauma care after SCI is challenging; after decompression surgery and spine stabilization, the first step is to assess the location and severity of the traumatic lesion. For this, clinical outcome measures are used to quantify the residual sensation and volitional control of muscles below the level of injury. These clinical assessments are important for decision-making, including the prediction of the recovery potential of individuals after the SCI. In clinical care, this quantification is usually performed using sensation and motor scores, a semi-quantitative measurement, alongside the binary classification of the sacral sparing (yes/no). Objective: In this perspective article, I review the use of surface EMG (sEMG) as a quantitative outcome measurement in subacute and chronic trauma care after SCI. Methods: Here, I revisit the main findings of two comprehensive scoping reviews recently published by our team on this topic. I offer a perspective on the combined findings of these scoping reviews, which integrate the changes in sEMG with SCI and the use of sEMG in neurorehabilitation after SCI. Results: sEMG provides a complimentary assessment to quantify the residual control of muscles with great sensitivity and detail compared to the traditional clinical assessments. Our scoping reviews unveiled the ability of the sEMG assessment to detect discomplete lesions (muscles with absent motor scores but present sEMG). Moreover, sEMG is able to measure the spontaneous activity of motor units at rest, and during passive maneuvers, the evoked responses with sensory or motor stimulation, and the integrity of the spinal cord and descending tracts with motor evoked potentials. This greatly complements the diagnostics of the SCI in the subacute phase of trauma care and deepens our understanding of neurorehabilitation strategies during the chronic phase of the traumatic injury. Conclusions: sEMG offers important insights into the neurophysiological factors underlying sensorimotor impairment and recovery after SCIs. Although several qualitative or semi-quantitative outcome measures determine the level of injury and the natural recovery after SCIs, using quantitative measures such as sEMG is promising. Nonetheless, there are still several barriers limiting the use of sEMG in the clinical environment and a need to advance high-density sEMG technology.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".