Design and development of a computer assisted glenoid implantation technique for shoulder replacement surgery
Bibliographic record
Abstract
OBJECTIVE: Replacement of the diseased shoulder joint with implants is a procedure whose frequency is rapidly increasing. However, glenoid replacement remains challenging due to the difficult joint exposure and visualization of anatomical reference landmarks during the procedure. Improper positioning of the glenoid component can lead to early failure. The objective of this study was to develop and evaluate a Computer Assisted Glenoid Implantation (CAGI) technique to achieve a more accurate and reliable placement of the glenoid component. MATERIALS AND METHODS: Twenty cadaveric scapulae were imaged with CT. The accuracy of an electromagnetic tracking system and 3D surface modeling for the measurement of glenoid position was compared to that of the standard CT-based method. Custom jigs were then developed to track instruments and to correct for scapular motion during in vitro trials. A standardized protocol for determining, in real time, the glenoid position and placement was developed and validated. RESULTS: The version angles measured by the tracking system, CT, and the 3D modeling software were 0.0 +/- 1.2 degrees , -1.3 +/- 1.0 degrees , and -1.1 +/- 1.1 degrees , respectively. The magnitudes for inclination angles were 0.7 +/- 0.7 degrees , 0.9 +/- 0.8 degrees , and 1.0 +/- 0.7 degrees , respectively. A statistically significant difference was found only between measurements made with the tracking system and with CT (p < 0.05). Testing of the CAGI system in a cadaveric trial resulted in an accuracy of 1.17 degrees of version and 0.60 degrees of inclination. The procedure was readily performed with excellent feedback and guidance for the surgeon. CONCLUSIONS: Preoperative planning using CT imaging with 3D modeling and intraoperative tracking were combined to produce improved accuracy and reliability of glenoid implantation in the setting of total shoulder arthroplasty.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".