Outcomes of patients undergoing anatomical total shoulder arthroplasty with augmented glenoid components – a systematic review
Bibliographic record
Abstract
Background: Glenoid loosening is an issue in anatomic total shoulder arthroplasty (a-TSA). This has been attributed to abnormal glenoid anatomy, common among these patients. Different alternatives have been proposed to tackle glenoid bone loss and restore joint alignment with augmented glenoid implants being increasingly used to deal with this problem. This systematic review aims to evaluate the clinical and radiological outcomes of patients undergoing augmented glenoid a-TSAs. Our hypothesis was that augmented glenoid components will lead to good patient outcomes with a low incidence of complications and revision procedures. Methods: MEDLINE, EMBASE, CENTRAL and CINHAL were searched from inception to February 2022 for information pertaining to outcomes of patients undergoing a-TSA with augmented glenoid implants. Results: Eighteen studies reported on outcomes of 814 a-TSA (800 participants) with a mean follow-up of 3.7 years. Most studies (67%) were Type IV level of evidence. Almost 70% of participants underwent an a-TSA secondary to primary glenohumeral osteoarthritis. Most glenoids were type B2 (73%). Augmented glenoids material was mostly all-polyethylene (81%) with full wedge (45%) and stepped components (38%) designs being the most common. Most studies reported good clinical outcomes. 17 patients (4%) underwent a revision surgery. Conclusions: Our review found that patients undergoing a-TSA with augmented glenoid components report good outcomes at short-to-mid-term follow-up. Further research is warranted to determine if such outcomes remain similar in long term. Level of evidence: Level III, Systematic Review of Therapeutic Studies.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 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".