Anatomical study of the innervation of glenohumeral and acromioclavicular joint capsules: implications for image-guided intervention
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: In 2011, chronic shoulder joint pain was reported by 18.7 million Americans. Image-guided radiofrequency ablation has emerged as an alternative intervention to manage chronic shoulder joint pain. To optimize the effectiveness of shoulder denervation, it requires a detailed understanding of the nerve supply to the glenohumeral and acromioclavicular joints relative to landmarks visible with image guidance. The purpose of this cadaveric study was to determine the origin, course, relationships to bony landmarks, and frequency of articular branches innervating the glenohumeral and acromioclavicular joints. METHODS: Fifteen cadaveric specimens were meticulously dissected. The origin, course, and termination of articular branches supplying the glenohumeral and acromioclavicular joints were documented. The frequency of each branch was determined and used to generate a frequency map that included their relationships to bony and soft tissue landmarks. RESULTS: In all specimens, the posterosuperior quadrant of the glenohumeral joint was supplied by suprascapular nerve; posteroinferior by posterior division of axillary nerve; anterosuperior by superior nerve to subscapularis; and anteroinferior by main trunk of axillary nerve. Less frequent innervation was found from lateral pectoral nerve and posterior cord. The acromioclavicular joint was found to be innervated by the lateral pectoral and acromial branch of suprascapular nerves in all specimens. Bony and soft tissue landmarks were identified to localize each nerve. CONCLUSIONS: The frequency map of the articular branches supplying the glenohumeral and acromioclavicular joints, as well as their relationship to bony and soft tissue landmarks, provide an anatomical foundation to develop novel shoulder denervation and perioperative pain management protocols.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".