Arthroscopic Suprascapular Nerve Release at the Suprascapular Notch in a Cadaveric Model: An Anatomic Approach
Bibliographic record
Abstract
Arthroscopic release of the suprascapular nerve at the suprascapular notch, to our knowledge, has rarely been described. The purpose of this study was to evaluate the feasibility and relevant anatomic landmarks in a cadaveric model that can be identified arthroscopically for reliable and reproducible arthroscopic release of the superior transverse scapular (STS) ligament. In 8 fresh-frozen cadaveric shoulders, arthroscopic release of the STS ligament was performed. The acromioclavicular joint is first identified while viewing through a posterior subacromial portal. The distal clavicle is then followed medially until the most lateral portion of the coracoclavicular (CC) ligaments (trapezoid ligament) is identified. The most medial margin of the CC ligaments (conoid ligament) is identified, and the trapezoid and conoid ligaments are dissected and identified individually. The conoid ligament is followed inferiorly and medially to the base of the coracoid. At the base of the coracoid, the confluence of the trapezoid and conoid ligaments (CC) and the STS ligament is identified. The STS ligament can be identified coursing horizontally across the field of view. The STS ligament may be incised by use of dissecting scissors through a lateral, accessory lateral, or accessory posterior portal, releasing the suprascapular nerve.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".