Cryoneurolysis for Managing Spasticity in Multiple Sclerosis: A Case Report Demonstrating Sustained Functional Gains and Cost-Effectiveness
Bibliographic record
Abstract
Cryoneurolysis is a minimally invasive procedure that induces secondary axonotmesis while preserving the nerve's supportive structures, offering an alternative approach to managing spasticity in multiple sclerosis (MS)-a condition affecting up to 90% of patients with MS and often leading to contractures, pain, and reduced mobility. In this case report, a 48-year-old woman with progressive MS who previously experienced side effects and limited benefits from prior botulinum toxin-A injections underwent ultrasound-guided percutaneous cryoneurolysis on targeted nerves to spastic muscles identified using the response to diagnostic nerve blocks. Range of motion and spasticity were assessed using the Modified Ashworth Scale, Modified Tardieu Scale, and numerical pain ratings. The patient demonstrated marked, sustained improvements in range of motion (shoulder flexion, abduction, and external rotation and elbow extension up to 11mo and knee flexion up to 7mo) and a reduction in spasticity (at 11mo for shoulder and elbow, 4mo for knee). The patient then experienced a progressive MS crisis, and a second treatment was offered at 1 year. There were additional gains after the second round of cryoneurolysis. The procedure showed 15-fold annual cost savings in their jurisdiction compared with botulinum toxin-A, underscoring the potential economic benefits of cryoneurolysis. These findings suggest that cryoneurolysis is a promising treatment for upper and lower limb spasticity in MS, providing significant, sustained improvements in range of motion, pain, and spasticity. Further research with larger cohorts is needed to confirm the long-term efficacy, broader applicability, and fully substantiate the cost-effectiveness of this emerging treatment modality.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".