Glenoid exposure in shoulder arthroplasty: the role of soft tissue releases
Bibliographic record
Abstract
BACKGROUND: The deltopectoral approach is commonly used in shoulder arthroplasty. Various soft tissue releases can be performed to obtain adequate glenoid exposure, but their effectiveness is not known. The purpose of this study was to (1) quantify the effects of various releases on the amount of glenoid surface area exposure and (2) determine if common soft tissue releases performed about the shoulder significantly improve exposure of the glenoid. METHODS: A standard deltopectoral approach was used on cadaveric shoulders (n=8) in the beach chair position. The releases performed were as follows: long head of biceps, pectoralis major tendon, inferior capsule, and posterior capsule. Following each release, a custom-designed jig was used to mark the exposed glenoid surface. The glenoid was then digitized using a 3D surface scanner to quantify the exposed surface area with each release. RESULTS: The mean glenoid surface area exposure prior to any releases was 57% (SD 8%). Following release of the long head of biceps, exposure increased to 69% (SD 10%). The exposed area was increased to 83% (SD 6%) with release of the pectoralis major, and 93% (SD 2%) with inferior capsule. The entire glenoid was exposed following posterior capsule release. CONCLUSIONS: Release of the long head of biceps, pectoralis major, and inferior and posterior capsule all independently led to significant increases in glenoid surface exposure in the deltopectoral approach. Mean surface area exposed with all 3 releases was 93%. Although posterior capsular release improved exposure, the results of this study suggest that this is rarely necessary.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".