Anatomical Evaluation of the Proximity of Neurovascular Structures During Arthroscopically Assisted Acromioclavicular Joint Reconstruction: A Cadaveric Pilot Study
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to examine the safety of an arthroscopic technique for acromioclavicular joint (ACJ) reconstruction by investigating its proximity to important neurovascular structures. METHODS: Six shoulders from 4 cadaveric specimens were used for ACJ reconstruction in this study. The procedure consists of performing an arthroscopic acromioclavicular (AC) reduction with a double button construct, followed by coracoclavicular ligament reconstruction without drilling clavicular tunnels. Shoulders were subsequently dissected in order to identify and measure distances to adjacent neurovascular structures. RESULTS: The suprascapular artery and nerve were the closest neurovascular structures to implanted materials. The mean distances were 8.2 (standard deviation [SD] = 3.6) mm to the suprascapular nerve and 5.6 (SD = 4.2) mm to the suprascapular artery. The mean distance of the suprascapular nerve from implants was found to be greater than 5 mm (P = .040), while the distance to the suprascapular artery was not (P > .5). Neither difference was statistically significant (P = .80 for artery; P = .08 for nerve). CONCLUSIONS: Mini-open, arthroscopically assisted ACJ reconstruction safely avoids the surrounding nerves, with no observed damage to any neurovascular structures including the suprascapular nerve and artery, and may be a viable alternative to open techniques. However, surgeons must remain cognizant of possible close proximity to the suprascapular artery. CLINICAL RELEVANCE: This study represents an evaluation of the safety and feasibility of a minimally invasive ACJ reconstruction as it relates to the proximity of neurovascular structures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".