Cryoneurolysis of the Femoral Nerve for Focal Spasticity in an Ambulatory Patient
Bibliographic record
Abstract
Introduction: Spasticity of the knee extensors is a common presentation among patients with multiple sclerosis. The resulting stiff leg gait can result in increased risk of falls, heightened energy expenditure during gait, lowered gait speed, and compensatory gait mechanisms that increase wear on the hips. Cryoneurolysis is a novel percutaneous, minimally invasive treatment for focal spasticity. Methods: A single patient with multiple sclerosis was treated with cryoneurolysis of the femoral nerve branch to rectus femoris. The patient was followed for 15 months. Spasticity severity, gait speed, and patient reported outcomes were collected at each follow-up. Results: Spasticity severity as per the Modified Ashworth Scale was reduced at 1 month, with change persisting up to 15 months post-procedure. Range of motion as per the Modified Tardieu Scale showed gradual improvement over the 15-month period. Gait speed increased after the procedure from 21.15 seconds to 12.49 seconds for the 10 m walk test 1 month post-procedure, then slowed to baseline after 15 months. The patient's confidence in their gait improved and their independence was maintained throughout the follow-up period. Because of the regression in the 10 m walk test, the patient elected to have the procedure repeated after 15 months. Immediately after the procedure, the 10 m test time improved to 16.20 seconds. Conclusion: Cryoneurolysis of the femoral nerve may be an effective, long-lasting treatment for spasticity causing stiff knee gait in patients with multiple sclerosis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".