Glenoid preparation in reverse shoulder arthroplasty: robotic arm–assisted preparation compared to manual preparation and patient-specific guides
Bibliographic record
Abstract
BACKGROUND: Precise and accurate glenoid preparation is important for the success of shoulder arthroplasty. Despite advancements in preoperative planning software and enabling technologies, most surgeons execute the procedure manually. Patient-specific instrumentation (PSI) facilitates accurate glenoid guide pin placement for cannulated reaming; however, few commercially available systems offer depth of reaming control. Robotic arm-assisted bone preparation has gained popularity in knee and hip arthroplasty, but at the present time there is limited information available on the use of robotics for shoulder arthroplasty. The purpose of this study was to compare glenoid preparation and final implant position using 3 techniques: manual, manual assisted with PSI, and robotic arm-assisted bone preparation. METHODS: Six shoulder surgeons participated in this study using 3 preparation techniques: (1) manual reaming, (2) manual reaming over a pin inserted using PSI, and (3) preparation using a robotic arm assist with an end-effector burr and haptic boundaries. Each surgeon randomly conducted each technique on 2 separate Bone Matrix glenoid models, for a total of 36 glenoid models tested. To compare the techniques, the final prepared Bone Matrix models underwent a computed tomographic scan with 3D virtual model generation. The prepared 3D virtual glenoid models were then compared to the preoperatively planned models. Parameters compared included deviations in version, inclination, anterior-posterior (AP) translation, superior-inferior (SI) translation, and depth of reaming. RESULTS: Regarding glenoid version with values reported as mean deviations from the preoperative plan, the robotic-assisted technique (1°) was significantly better than manual (9°, P < .001) and PSI (4°, P < .001) techniques at executing the preoperative plan. Regarding inclination, the robotic-assisted technique (2°) was significantly better than manual (9°, P = .003) but not significantly different than PSI (3°, P = .211). The robotic arm technique, with AP translation, resulted in significantly lower mean displacements (0.3 mm) than the manual technique (2 mm, P = .001) and the PSI technique (2 mm, P = .002). With SI translation, the robotic arm-assisted technique (0.7 mm) resulted in significantly lower mean displacements as compared to the manual (2 mm, P = .007) and PSI (1 mm, P = .011). The robotic arm-assisted technique (0.4 mm) did not result in significantly lower mean depth of reaming displacements compared to the manual technique (0.8 mm, P = .051) but did when compared to PSI (0.8 mm, P = .036). CONCLUSIONS: Glenoid preparation using a robotic arm with an end-effector burr and haptic boundaries was significantly better in its ability to execute a preoperatively planned implant position than manual preparation in 4 of the 5 glenoid metrics examined and was significantly better than PSI in 4 of the 5 glenoid metrics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".