A Case Report Illustrating the Combined Use of Cryoneurolysis and Percutaneous Needle Tenotomy in the Treatment of Longstanding Spastic Shoulder Contractures After Stroke
Bibliographic record
Abstract
Adduction and internal rotation of the shoulder is a common presentation in post-stroke patients, and can often be caused by spasticity and musculotendinous retraction causing a contracture of the pectoralis major and minor muscles. A post cerebral arteriovenous malfunction rupture patient with severe refractory left shoulder spasticity with contracture was treated with cryoneurolysis to the medial and lateral pectoral nerves, combined with a percutaneous needle tenotomy to the pectoralis major tendon. There was an improvement in shoulder forward flexion, abduction and external rotation immediately and found sustained at 8 weeks by 50°, 45°, and 15°. The patient noted an immediate cessation of limitation of shoulder abduction, a liberation of range of motion of the shoulder, and looseness in their arm and shoulder. They reported a dramatic improvement in their gait, increased independence, and an improvement in overall quality of life in a structured interview 8 weeks after the procedure. The patient relayed a positive experience with the combined neuro-orthopedic procedure of cryoneurolysis and tenotomy for the treatment of their spastic shoulder. This combined treatment could be considered as a management strategy for patients experiencing shoulder spasticity with contracture.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".