Lateralization in Reverse Shoulder Arthroplasty
Bibliographic record
Abstract
Indications for Reverse Shoulder Arthroplasty (RSA) have been extended over the last 25 years, and RSA has become the most frequently implanted shoulder arthroplasty worldwide. The initial Grammont design with medialization of the joint center of rotation (JCOR), placement of the JCOR at the bone-implant interface, distalization and semi-constrained configuration has been associated with drawbacks such as reduced rotation and range of motion (ROM), notching, instability and loss of shoulder contour. This review summarizes new strategies to overcome these drawbacks and analyzes the use of glenoid-sided, humeral-sided or global bipolar lateralization, which are applied differently by surgeons and current implant manufacturers. Advantages and drawbacks are discussed. There is evidence that lateralization addresses the initial drawbacks of the Grammont design, improving stability, rates of notching, ROM and shoulder contour, but the ideal extent of lateralization of the glenoid and humerus remains unclear, as well as the maximal acceptable joint reaction force after reduction. Overstuffing and spine of scapula fractures are potential risks. CT-based 3D planning as well as artificial intelligence will help surgeons with planning and execution of appropriate lateralization in RSA. Long-term follow-up of lateralization with new implant designs and implantation strategies is needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".