Spasticity After Spinal Cord Injury: An Evidence-Based Review of Current Interventions
Bibliographic record
Abstract
Purpose: To provide an overview of evidence for spinal cord injury (SCI) spasticity interventions in peerreviewed, published literature. Method: Structured review and synthesis of spasticity treatments in the literature. Each publication was rated according to the Downs and Black methodology for assessing nonrandomized studies and according to the Physiotherapy Evidence Database (PEDro) scale for assessing randomized controlled trials. Results: Level 1 evidence supports the use of transcutaneous electrical nerve stimulation (TENS), penile vibration, baclofen, tizanidine, clonidine, cyproheptadine, gabapentin, and Lthreonine to reduce spasticity in SCI. Conclusion: Although spasticity is a common complication following SCI, there is relatively little evidence for the treatment of spasticity and even less evidence that has been confirmed by independent replication. TENS is the only routine nonpharmacological treatment for spasticity that is supported by adequate level 1 evidence. Several pharmacological treatments, including baclofen, tizanidine, and clonidine, are supported by level 1 evidence. There is level 1 evidence to support test doses of intrathecal baclofen for the short-term reduction of spasticity in SCI but not for its long-term use. The lack of level 1 evidence for spasticity interventions does not necessarily reflect a lack of effective treatments, but it does emphasize the need for further studies. All other interventions reviewed would benefit from further study.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".