Superior glenoid inclination in shoulder arthroplasty: a comparison of standard to superiorly augmented glenoid implants
Bibliographic record
Abstract
The success of anatomic total shoulder arthroplasty depends on glenoid component stability, with loosening responsible for nearly half of revision surgeries. Superior glenoid inclination exacerbates component loosening by promoting unbalanced loading by the humeral head. This study investigates a novel superiorly augmented glenoid component designed to correct excessive superior inclination. We hypothesized that increasing augmentation would limit superior humeral head migration and asymmetric loading, thereby increasing compressive rather than tensile stresses at the implant–bone interface. Finite element models of 8 scapulae with glenohumeral osteoarthritis were fitted with a standard 4-peg component and 3 superiorly augmented components (5°, 10°, and 15° augment angles). Loads angled 25° inferiorly to 25° superiorly were applied. Outcomes included superior–inferior humeral head position and implant–bone interface stresses. For every 5° increase in augmentation, the humeral head translated inferiorly by 0.4 ± 0.1 mm for the 15° native inclination group, 0.3 ± 0.1 mm for 10°, and 0.2 ± 0.1 mm for 5°. Translations ranged between 0.2 mm and 1.0 mm, consistent with previous findings. The humeral head position in the fully corrected state differed significantly from uncorrected states in each native inclination group ( P < .01). The percentage of implant–bone interface area in compression increased by 4.3 ± 0.6% per 5° correction for the 15° native inclination group, 2.6 ± 0.6% for 10°, and 1.8 ± 1.9% for 5°. Superior augmentation restored humeral head centralization and increased the proportion of the implant–bone interface in compression, decreasing liftoff potential and reducing mechanical risk factors for loosening in patients with excessive superior inclination.
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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) | 0.001 | 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".