Mixed reality visualization in shoulder arthroplasty: is it better than traditional preoperative planning software?
Bibliographic record
Abstract
BACKGROUND: Preoperative traditional software planning (TSP) is a method used to assist surgeons with implant selection and glenoid guide-pin insertion in shoulder arthroplasty. Mixed-Reality (MR) is a new technology that uses digital holograms of the preoperative plan and guide-pin trajectory projected into the operative field. The purpose of this study was to compare TSP to MR in a simulated surgical environment involving insertion of guide-pins into models of severely deformed glenoids. METHODS: Eight surgeons inserted guide-pins into eight randomized three-dimensional-printed severely eroded glenoid models in a simulated surgical environment using either TSP or MR. In total, 128 glenoid models were used and statistically compared. The outcomes compared between techniques included procedural time, difference in guide-pin start point, difference in version and inclination, and surgeon confidence via a confidence rating scale. RESULTS: When comparing traditional preoperative software planning to MR visualization as techniques to assist surgeons in glenoid guide pin insertion, there were no statistically significant differences in terms of mean procedure time (P=0.634), glenoid start-point (TSP = 2.2 ± 0.2 mm , MR = 2.1 ± 0.1 mm; P=0.760), guide-pin orientation (P=0.586), or confidence rating score (P=0.850). CONCLUSIONS: The results demonstrate that there were no significant differences between traditional preoperative software planning and MR visualization for guide-pin placement into models of eroded glenoids. A perceived benefit of MR is the real-time intraoperative visibility of the surgical plan and the patient's anatomy; however, this did not translate into decreased procedural time or improved guide-pin position. Level of evidence: Basic Science Study; Biomechanics.
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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 | 0.007 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".