Protocol for Ultrasound-Guided Posterior Glenohumeral Joint Capsule Acupotomy Procedure in Adhesive Capsulitis
Bibliographic record
Abstract
Background: Adhesive Capsulitis (Frozen Shoulder) is characterized by pain and limited range of motion (ROM) in the shoulder, often caused by fibrosis and adhesions in the articular capsule.Ultrasound-guided interventions, particularly acupotomy, offer a minimally invasive approach to managing these conditions by targeting specific anatomical structures under visualization.Objectives: This study aimed to develop a standardized protocol for ultrasound-guided posterior shoulder capsule acupotomy in patients with adhesive capsulitis, focusing on precise localization and safe execution.Methods: The protocol involved identifying key anatomical structures such as the Axillary Pouch, Posterior Glenohumeral Joint Capsule, and Superior Glenohumeral Joint Capsule using ultrasound.Patients with ROM limitations were categorized based on their movement restrictions, and acupotomy was performed under ultrasound guidance at specific target sites.Key safety measures, including avoiding nerves and vessels, were integrated.Results: The protocol demonstrated effective localization of target structures and improved ROM in patients with adhesive capsulitis.No complications such as vascular or neural injury were observed.The use of ultrasound enhanced procedural precision and safety.Conclusion: Ultrasound-guided acupotomy provides a reliable and safe method for treating Adhesive Capsulitis, particularly during the frozen phase.By offering clear visualization and accurate targeting, this protocol enhances the efficacy and safety of interventions, addressing limitations of traditional techniques.Further research is needed to validate this protocol across diverse patient populations.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".