Glenoid bone grafting in primary anatomic total shoulder arthroplasty: a systematic review
Bibliographic record
Abstract
BACKGROUND: Primary anatomic total shoulder arthroplasty can be challenging in patients with complex glenoid wear patterns and bone loss. Severe retroversion (>15°) or significant bone loss may require bone grafting. This review summarizes the rate of revision and long-term outcomes of anatomic total shoulder arthroplasty with bone graft. METHODS: A systematic search of MEDLINE, Embase, PubMed, and CENTRAL databases was conducted from the date of inception to 23 October 2018. Two reviewers independently screened articles for eligibility and extracted data for analysis. The primary outcome was rate of revision. The secondary outcomes were rate of component loosening, functional outcome, and range of motion. RESULTS: Of the 1056 articles identified in the search, 26 underwent full-text screening and 7 articles were included in the analysis. All procedures were one-stage anatomic total shoulder arthroplasties. The rate of revision was 5.4% with component loosening and infection listed as indications over a weighted mean follow-up period of 6.3 years. Complications occurred in 12.6% of patients. CONCLUSION: Glenoid bone grafting in anatomic total shoulder arthroplasty results in comparable revision rates and improvement in pain compared to augmented glenoid components and reverse shoulder arthroplasty. Due to the low quality of evidence, further prospective studies should be conducted. LEVEL OF EVIDENCE: IV.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".