Adjusting Implant Size and Position Can Improve Internal Rotation After Reverse Total Shoulder Arthroplasty in a Three-dimensional Computational Model
Bibliographic record
Abstract
BACKGROUND: Efforts during reverse total shoulder arthroplasty (RSA) have typically focused on maximizing ROM in elevation and external rotation and avoiding scapular notching. Improving internal rotation (IR) is often overlooked, despite its importance for functional outcomes in terms of patient self-care and hygiene. Although determinants of IR are multifactorial, it is unable to surpass limits of bony impingement of the implant. Identifying implant configurations that can reduce bony impingement in a computer model will help surgeons during preoperative planning and also direct implant design and clinical research going forward. QUESTIONS/PURPOSES: In a CT-modeling study, we asked: What reverse total shoulder arthroplasty implant position improves the range of impingement free internal rotation without compromising other motions (external rotation and extension)? METHODS: CT images stored in a deidentified teaching database from 25 consecutive patients with Walch A1 glenoids underwent three-dimensional templating for RSA. Each template used the same implant and configuration, which consisted of an onlay humeral design and a 36-mm standard glenosphere. The resulting constructs were virtually taken through ROM until bony impingement was found. Variations were made in the RSA parameters of baseplate lateralization, glenosphere size, glenosphere overhang, humeral version, and humeral neck-shaft angle. Simulated ROM was repeated after each parameter was changed individually and then again after combining multiple changes into a single configuration. The impingement-free IR was calculated and compared between groups. We also evaluated the effect on other ROM including external rotation and extension to ensure that configurations with improvements in IR were not associated with losses in other areas. RESULTS: Combining lateralization, inferiorization, varus neck-shaft angle, increased glenosphere size, and increased humeral anteversion resulted in a greater improvement in internal rotation than any single parameter change did (median baseline IR: 85° [interquartile range 73° to 90°]; combined changes: 119° [IQR 113° to 121°], median difference: 37° [IQR 32° to 43°]; p < 0.001). CONCLUSION: Increased glenosphere overhang, varus neck-shaft angle, and humeral anteversion improved internal rotation in a computational model, while glenoid lateralization alone did not. Combining these techniques led to the greatest improvement in IR. CLINICAL RELEVANCE: This computer model study showed that various implant changes including inferiorization, varus neck-shaft angle, increased glenosphere size, and increased humeral anteversion can be combined to increase impingement-free IR. Surgeons can employ these currently available implant configurations to improve IR when planning and performing RSA. These findings support the need for further clinical studies validating the effect of implant configuration on resultant IR.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".