Glenoid component positioning and guidance techniques in anatomic and reverse total shoulder arthroplasty: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Positioning of the glenoid component is one of the most challenging steps in shoulder arthroplasty, and prosthesis longevity as well as functional outcomes is considered highly dependent on accurate positioning. This review considers the evidence supporting surgical navigation and patient-specific instruments for glenoid implant positioning in anatomic and reverse total shoulder arthroplasty. METHODS: A systematic literature search was performed for studies assessing glenoid implant positioning accuracy as measured by cross-sectional imaging on live subjects or cadaver models. Meta-analysis of controlled studies was performed to estimate the primary effects of navigation and patient-specific instruments on glenoid implant positioning error. Meta-analysis of absolute positioning outcomes was also performed for each group incorporating data from controlled and uncontrolled studies. RESULTS: Nine studies, four controlled and five uncontrolled, with 258 total subjects were included in the analysis. Meta-analysis of controlled studies supported that both navigation and patient-specific instruments had a moderate statistically significant effect on improving glenoid implant positioning outcomes. Meta-analysis of absolute positioning outcomes demonstrates glenoid implant positioning with standard instrumentation results in a high rate of malposition. DISCUSSION: Navigation and patient-specific instruments improve glenoid positioning outcomes. Whether the improvement in positioning outcomes achieved translate to better clinical outcomes is unknown.
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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.017 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".