Analysis and 3D correction of glenoid dysplasia with metal hemi-wedge base plate augment: short-term radiographic outcomes
Bibliographic record
Abstract
BACKGROUND: Glenoid defects can be addressed traditionally by asymmetric reaming or by bone-preserving correction to a more lateral joint line by bone or metal augmented baseplates in reverse shoulder arthroplasties. While there is more evidence in literature regarding the outcome and complications of Bony Increased Offset Reversed Shoulder Arthroplasty (BIO-RSA), there is minimal reported experience with the outcome after metal glenoid augments. The aim of this study was to determine whether a metal augment can correct the glenoid deformity in an anatomic manner. METHODS: Glenoid morphology and deformity were determined in 50 patients with Walch type B1, B2, D and Favard type E0-E3 glenoid defects using preoperative radiographic and computed tomography (CT) analysis. All patients received a preoperative planning CT with 3D planning, and measurements of glenoid inclination (in 3 planes proximal, middle, distal), reversed shoulder arthroplasty angle (RSA) and glenoid version were obtained. All patients had a pathologic inclination in the coronal or frontal planes of > 10°. Above the threshold of 10° pathological glenoid version or inclination metal hemi-augments of 10°, 20°, or 30° were used which allow an individual 360° augment positioning according to the patient glenoid deformity. RESULTS: The mean preoperative numbers of the glenoid version demonstrate that most glenoids were in retroversion and superior inclination. In total 2410° wedges, 1820° wedges and 8 30° wedges were used. In the majority of cases, the wedge was positioned posteriorly and/or cranially between 10:00 and 12:00 o'clock, which allows a correction in a 3D manner of the glenoid inclination and version. The mean RSA angle could be corrected from 22.76 ± 6.06 to 0.19° ± 2.7 (p < 0.0001). The highest retroversion of the glenoid is evidenced in the proximal section and it could be corrected from - 23.32° ± 4.56 to - 6.74° ± 7.75 (p < 0.0001) and in the middle section from - 18.93° ± 3.35 to - 7.66° ± 5.28 (p < 0.0001). A mean sphere bone overhang distance (SBOD) of 5.70 ± 2.04 mm was found in order to avoid or minimize relevant scapular notching. CONCLUSION: By using a new 360° metal-augmented baseplate, the preoperative pathological inclination and retroversion can be corrected without medialization of the joint line. Future clinical results will show whether this bone-preserving procedure improves also the clinical outcomes as compared to asymmetric medialized reaming or wedged BIO-RSA. LEVEL OF EVIDENCE: Level IV, Case series.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".