Glenoid bone loss in shoulder arthroplasty: a narrative review
Bibliographic record
Abstract
Background and Objective: Crucial to the success of any total or reverse shoulder arthroplasty (RSA) is the stability of the glenoid component fixation. Instability can lead to early implant failure and unsatisfactory results. Patients often present with varying forms of glenoid bone loss (GBL) in both the primary and revision settings, which can be a challenge for the treating surgeon. Severe cases of GBL can increase the risk of potential complications and diminish implant longevity. The use of the reverse total shoulder replacement has been particularly helpful when addressing significant glenoid bony defects. Various approaches have been proposed to deal with GBL, all of which require an individualized assessment of the specifics of the defect in order to provide maximal fixation and thereby optimize the longevity of the shoulder arthroplasty. This article aims to review the recent literature on GBL in shoulder arthroplasty to provide guidance when considering treatment based on the best available evidence. Methods: PubMed, MEDLINE, EMBASE, AccessMedicine, ClinicalKey, DynaMed, and Micromedex were queried for publications utilizing the following keywords: "glenoid bone loss" AND "glenoid bone deficiency" AND "shoulder arthroplasty" AND "classification". The search was restricted to research published between 2004 and 2023. There were no restrictions on study type or language. Key Content and Findings: GBL should be critically evaluated prior to undertaking total shoulder arthroplasty (TSA). The treating surgeon should be aware of various methods of addressing bone defects. Conclusions: The use of TSA is increasing to address various shoulder pathologies. Addressing glenoid bone defects is of critical importance to maximize the longevity and outcome of 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".