A comparison of patient-specific instrumentation to navigation for conducting humeral head osteotomies during shoulder arthroplasty
Bibliographic record
Abstract
Background The humeral head osteotomy during shoulder arthroplasty influences humeral component height, version and possibly neck-shaft angle. These parameters all potentially influence outcomes of anatomic and reverse shoulder replacement to a variable degree. Patient-specific guides and navigation have been studied and utilized clinically for glenoid component placement. Little, however, has been done to evaluate these techniques for humeral head osteotomies. The purpose of this study, therefore, was to evaluate the use of patient-specific guides and surgical navigation for executing a planned humeral head osteotomy. Methods The DICOM images of 10 shoulder computed tomography scans (5 normal and 5 osteoarthritic) were used to print 3D polylactic models of the humerus. Each model was duplicated, such that there were 2 identical groups of 10 models. After preoperative planning of a humeral head osteotomy, Group 1 underwent osteotomy via a patient-specific guide, while group 2 underwent a real time navigated osteotomy with an optically tracked sagittal saw. The cut height (millimeters), version (degrees) and neck-shaft angle (degrees) were recorded and statistically compared between groups. Results There were no statistically significant differences between patient-specific guides and navigation for osteotomy cut height ( P = .45) and humeral version ( P = .059). Navigation, however, resulted in significantly less neck-shaft angle error than the patient specific guides ( P = .023). Subgroup analysis of the osteoarthritic cases showed statistical significance for navigation resulting in less version error than the patient specific guides ( P = .048). Conclusion No significant differences were found between patient specific guides and navigation for recreation of the preoperatively planned humeral head cut height and version. Neck-shaft angle, however, had significantly less deviation from the preoperative plan when conducted with navigation.
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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 | 0.008 |
| 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) | 0.001 | 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".