Spasticity Predicts Motor Recovery for Patients with Subacute Motor Complete Spinal Cord Injury
Bibliographic record
Abstract
OBJECTIVE: A motor complete spinal cord injury (SCI) results in the loss of voluntary motor control below the point of injury. Some of these patients can regain partial motor function through inpatient rehabilitation; however, there is currently no biomarker to easily identify which patients have this potential. Evidence indicates that spasticity could be that marker. Patients with motor complete SCI who exhibit spasticity show preservation of descending motor pathways, the pathways necessary for motor signals to be carried from the brain to the target muscle. We hypothesized that the presence of spasticity predicts motor recovery after subacute motor complete SCI. METHODS: Spasticity (Modified Ashworth Scale and pendulum test) and descending connectivity (motor evoked potentials) were tested in the rectus femoris muscle in patients with subacute motor complete (n = 36) and motor incomplete (n = 30) SCI. Motor recovery was assessed by using the International Standards for Neurological Classification of Spinal Cord Injury and the American Spinal Injury Association Impairment Scale (AIS). All measurements were taken at admission and discharge from inpatient rehabilitation. RESULTS: We found that motor complete SCI patients with spasticity improved in motor scores and showed AIS conversion to either motor or sensory incomplete. Conversely, patients without spasticity showed no changes in motor scores and AIS conversion. In incomplete SCI patients, motor scores improved and AIS conversion occurred regardless of spasticity. INTERPRETATION: These findings suggest that spasticity represents an easy-to-use clinical outcome that might help to predict motor recovery after severe SCI. This knowledge can improve inpatient rehabilitation effectiveness for motor complete SCI patients. ANN NEUROL 2023.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.002 | 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".