Hybrid Glenoid Designs in Anatomic Total Shoulder Arthroplasty: A Systematic Review
Bibliographic record
Abstract
Background: Hybrid glenoid components in total shoulder arthroplasty (TSA) utilize both polyethylene and metal components to provide short-term stability and long-term biologic fixation through bone ingrowth. Questions/Purpose: We sought to systematically review the literature for studies that assessed outcomes of TSA performed using hybrid glenoid components. Methods: PubMed, Medline, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Embase were searched systematically for articles measuring clinical and patient-reported outcomes and rates of complication and revision following TSA using a hybrid glenoid component. Results: Seven studies with 593 shoulders were included in this review. The mean age of patients was 65 ± 1 years, and 46% of the population was male. Mean follow-up was 50 months (4.2 years). The overall complication rate was 7% and rate of revision was 2.5%; glenoid radiolucency was present in 33% of shoulders at mean follow-up of 50 months. Mean improvements in forward elevation, external rotation, internal rotation score, and abduction were 49°, 28°, 2 points, and 42°, respectively. Mean improvements in Constant, American Shoulder and Elbow Surgeons (ASES), and University of California, Los Angeles (UCLA) scores were 36 points, 52 points, and 17 points, respectively. Conclusion: Our review found that TSA using hybrid glenoid components results in low rates of complication and revision at early follow-up. Long-term studies are warranted to understand more fully the role of hybrid glenoid components in TSA.
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 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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".