Development and ex-vivo assessment of a novel patient specific guide and instrumentation system for minimally invasive total shoulder arthroplasty
Bibliographic record
Abstract
OBJECTIVE: To develop and assess a novel guidance technique and instrumentation system for minimally invasive short-stemmed total shoulder arthroplasty that will help to reduce the complications associated with traditional open replacement such as poor muscle healing and neurovascular injury. We have answered key questions about the developed system including (1) can novel patient-specific guides be accurately registered and used within a minimally invasive environment?; (2) can accuracy similar to traditional techniques be achieved? METHODS: A novel intra-articular patient-specific guide was developed for use with a new minimally invasive posterior surgical approach that guides bone preparation without requiring muscle resection or joint dislocation. Additionally, a novel set of instruments were developed to enable bone preparation within the minimally invasive environment. The full procedure was evaluated in six cadaveric shoulders, using digitizations to assess accuracy of each step. RESULTS: Patient-specific guide registration accuracy in 3D translation was 2.2±1.2mm (RMSE±1 SD; p = 0.007) for the humeral component and 2.7±0.7mm (p<0.001) for the scapula component. Final implantation accuracy was 2.9±3.0mm (p = 0.066) in translation and 5.7-6.8±2.2-4.0° (0.001<p<0.009) across the humerus implants' three rotations. Similarly, the glenoid component's implantation accuracy was 3.0±1.7mm (p = 0.008) in translation and 2.3-4.3±2.2-4.4° (0.008<p<0.09) in rotation. CONCLUSION: This system achieves minimally invasive shoulder replacement with accuracy similar to traditional open techniques while avoiding common causes of complications. SIGNIFICANCE: This novel technique could lead to a paradigm shift in shoulder arthroplasty for patients with moderate arthritis, which could significantly improve rehabilitation and functional outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".