Clinical, functional and radiographic outcomes of inverted-bearing reverse shoulder arthroplasty at minimum two year follow-up
Bibliographic record
Abstract
Background Inverted-Bearing Reverse Shoulder Arthroplasty (IB-RSA) is an alternative reverse bearing construct characterized by an ultra-high molecular weight polyethylene glenosphere combined with a CoCr metallic humeral liner. This concept was designed both to minimize bearing wear as well as reduce the potential for scapular notching seen with more traditional-bearing RSA systems. This study reports on clinical outcomes, functional scores, pain scores, and radiographic incidence of scapular notching in a series of IB-RSA at a minimum of two-year follow-up. Methods A retrospective study was conducted on patients who underwent an IB-RSA between 2016 and 2019, with a minimum follow-up period of two years. Patients were evaluated clinically for DASH score (DS), American Shoulder and Elbow Surgeons score (ASES), EQ-5D Health Questionnaire (EQ-5D), Global Rating of Change score (GRC), Single Assessment Numeric Evaluation score (SANE) and range of motion (ROM). Presence and grade of radiographic scapular notching was assessed using the classification of Sirveaux at 6,12, 24 months and at the last x-ray available. Results 61 consecutive patients were assessed at a mean postoperative follow-up of 37 months (range, 24-72 months). IB-RSA exhibited high overall outcome scores including DASH (38.3 +/-5), ASES (83 +/- 6), GRC (4 +/-0.6), and SANE (83 +/-11). ROM was assessed in 41 patients with 135 +- 21 of elevation, 23 +- 12 of external rotation and 5 +- 2 for internal rotation. Scapular notching was radiographically present in 23 (38%) patients (twenty grade 1, three grade 2) in the final follow-up, with all cases showing evidence of mechanical notching while no grade 3 or 4 cases were observed. The scapular notching didn't progress in most of the patients after the first year (P>0.05). The presence of scapular notching did not influence on clinical outcome scores as DASH, ASES, EQ-5D, GRC, SANE (P>0.05) or active range of motion (P>0.05). Conclusion IB-RSA demonstrates high patient-reported and functional outcome scores at a minimum of two years follow-up. We report only low-grade scapular notching with little progression after the first year. There was no correlation between scapular notching and clinical outcomes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".