Patient reported outcomes and ranges of motion after reverse total shoulder arthroplasty with and without subscapularis repair
Bibliographic record
Abstract
Background: In performing reverse total shoulder arthroplasty (rTSA), the role of repairing the subscapularis has been debated. Our objective was to determine the effect of subscapularis repair following rTSA on postoperative shoulder ranges of motion and patient reported outcome scores (PROs). Methods: A prospective registry was reviewed to establish a cohort of primary rTSA patients with a 135-degree humeral implant, with a minimum of 2 years of follow-up. Variables collected included demographics, subscapularis repair information, diagnosis, glenosphere size, and glenoid lateralization information. Outcomes collected were range of motion measurements, subscapularis strength, and multiple generic and shoulder PROs. Multivariable linear regression models were created to predict these 2-year outcomes. Results: The 143-patient cohort had a mean age of 69 years with 68% of patients undergoing subscapularis repair. After adjustment in the multivariable models, whether the subscapularis was repaired did not significantly predict a 2-year forward elevation, external rotation, internal rotation, subscapularis strength, Western Ontario Osteoarthritis of the Shoulder score, VR-12 scores, Constant Score, or American Shoulder and Elbow Surgeons Shoulder Scores. Increased glenoid lateralization significantly predicted greater internal rotation, higher VR-12 physical score, and higher Constant Score. There were no dislocations in either group. Conclusions: After adjusting for patient and implant factors, subscapularis repair was not associated with a 2-year postoperative range of motion, strength, or any PROs suggesting that repairing the subscapularis may not affect functional outcome. Increased glenoid lateralization through the baseplate and glenosphere independently predicted better internal rotation, VR-12 physical score, and Constant Scores indicating a benefit to lateralization during rTSA.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".