A comparison of pegged vs. keeled glenoid components regarding functional and radiographic outcomes in anatomic total shoulder arthroplasty: a systematic review and meta-analysis
Bibliographic record
Abstract
Background The number of total shoulder arthroplasties (TSAs) performed is increasing annually, with a continued effort to improve outcomes using new techniques and materials. In anatomic TSAs, the main options for glenoid fixation currently involve keeled or pegged components. The aim of this review was to determine which fixation option provides optimal long-term functional outcomes with decreased rates of revision surgery and radiolucency. Methods The MEDLINE, Embase, PubMed, and Cochrane databases were searched from 2007 to July 10, 2017, for all articles that examined TSAs using either pegged or keeled glenoid fixations. All studies were screened in duplicate for eligibility. Two separate analyses were completed examining noncomparative and comparative studies independently. Results A total of 7 comparative studies and 25 noncomparative studies were included in the final analysis. Included in the analysis were 4 randomized (level I) studies, 1 level II study, 8 level III studies, and 19 level IV studies. Meta-analysis of the comparative studies demonstrated a higher rate of revision surgery with keeled fixations compared with pegged fixations (odds ratio, 6.22; 95% confidence interval [CI], 1.38-28.1; P = .02). No significant difference was found with respect to functional outcomes, such as the American Shoulder and Elbow Surgeons score (mean difference, 9.54; 95% CI, –8.25 to 27.34; P = .29) and Constant score (mean difference, 5.31; 95% CI, –12.28 to 22.89; P = .55), as well as radiolucency rates (odds ratio, 1.89; 95% CI, 0.56−6.39; P = .30). Conclusion Pegged glenoid fixation may result in a decreased risk of revision TSAs, but no significant differences in patient-reported outcomes have been identified to date.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".