Accuracy of Glenoid Component Positioning in Reverse Shoulder Arthroplasty: A Biomechanical Comparison between 3D Preoperative Planning, PSI, Computer-Assisted Navigation, and Mixed Reality Navigation
Bibliographic record
Abstract
Accurate glenoid component positioning in shoulder arthroplasty is important to avoid potential impingement, loosening, and instability. Several techniques are currently utilized to assist in glenoid guide pin positioning, although no studies exist that directly compare the accuracy between these techniques. The objective of this study was to compare guide pin insertion accuracy using traditional 3D software planning (TSP), patient specific instrumentation (PSI) guides, computer-navigation (C- NAV), and mixed reality navigation (MR-NAV). Twenty shoulder computer tomography scans exhibiting glenohumeral arthritis or rotator cuff tear arthropathy were preoperatively planned for reverse shoulder arthroplasty. Quadruplicate models of each glenoid were plastic 3D printed and were used to randomly assess four guide pin insertion techniques by a fellowship trained surgeon as follows: (1) TSP, (2) PSI guides, (3) C-NAV, and (4) MR-NAV. Following guide pin placement, the absolute error in guide pin position and orientation relative to the preoperative plan was measured using a digitization system. Similar inclination (P>0.066) and version (P>0.515) accuracy occurred between PSI, C-NAV, and MR-NAV techniques. Furthermore, all three methods exhibited significantly less error in guide pin inclination compared to TSP (P<0.025). Greater version error was also observed with TSP (4±3°) but was not significantly greater than the other techniques (P>0.063). The error in guide pin entry point was similar between all four methods utilized (P>0.086). This study showed that the accuracy of PSI, C-NAV, and MR-NAV are superior to TSP for glenoid pin insertion in-vitro. Further investigation is needed to validate the accuracy of all guide pin insertion techniques in-vivo.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".