Influence of the level and strategy of glenoid component lateralization on the postoperative biomechanics of shoulder arthroplasty: a finite element analysis
Bibliographic record
Abstract
Background: Reverse total shoulder arthroplasty (rTSA) is widely used to treat fractures and osteoarthritis. Recent techniques enable humeral lateralization through glenoid-based augmentations. The biomechanical impacts of different lateralization strategies remain unclear. We aimed to quantify postoperative biomechanics and bone constraints under various strategies. Methods: A detailed finite element model of the scapulohumeral joint, including bones and active muscles, was used to perform 12 numerical rTSAs. Two levels of lateralization were achieved through baseplate or glenosphere lateralization, combined with 3 glenosphere diameters. Three motions-abduction, flexion, and extension-were simulated by activating muscle sets. Humeral kinematics, glenohumeral forces and moments, bone stresses, and scapular notching were analyzed. Results: Greater lateralization improved range of motion by enlarging the acromiohumeral space but did not enhance muscle elevation efficiency. Larger glenospheres improved initial stability through better muscle resting tension. Greater lateralization, by either method, increased bone stresses, notably at the acromion. Lateralization altered rotator cuff muscle efficiency and should be adapted to patient-specific weaknesses. Greater lateralization led to a more medial humeral final position. Under linearly increasing muscle forces, the humeral path was nonlinear, reflecting poor control without fine motor control. Opposite biomechanical trends occurred in extension versus abduction/flexion when lateralization increased. Extension was most prone to scapular neck impingement, leading to notching and instability. Conclusion: This finite element study highlights that different lateralization strategies in rTSA lead to distinct biomechanical behaviors, influencing bone stress and potentially affecting the risk of scapular spine fracture.
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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 | 0.000 | 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".