Cryoanalgesia for shoulder pain: a motor-sparing approach to rotator cuff disease
Bibliographic record
Abstract
INTRODUCTION: Rotator cuff disease is a common cause of musculoskeletal pain and disability, and the management can be challenging. Joint denervation emerges as a new technique, but the literature on shoulder neural ablation procedure is largely limited to pulsed radiofrequency due to the concern of motor impairment. We described a novel motor-sparing approach of cryoablation for the management of shoulder pain based on the recent literature on the innervation of shoulder. METHODS: Four patients with a history of rotator cuff disease refractory to conservative therapy and not amenable to surgery underwent a ultrasound-guided cryoablation of the capsular branches of the shoulder joint after a positive diagnostic injection. The target articular branches were based on the anatomical landmarks described in recent publication. They were the acromial, superior and inferior branches of the suprascapular nerve, the anterior branch of the axillary nerve, the nerve to the subscapularis, which were all located around the superior, posterior and anterior glenoid. The lateral pectoral nerve articular branch was targeted at the coracoclavicular space. RESULTS: All four patients experienced at least 60% pain relief with improvement in function for 6-12 months following the procedure without any clinical evidence of motor impairment. No adverse effect was observed. DISCUSSION: Based on the current understanding of the glenohumeral joint articular branches and their relationship to the bony landmark, targeting the articular branches only was feasible and led to good outcomes. Further large prospective cohort study is needed.
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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 | 0.000 | 0.001 |
| 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".